GcatWiki - User contributions [en]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:47:11Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a way of testing and improving entire systems in a nonbiased manner as they try to make synthetic constructs and optimal model agree.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T21:26:50Z

Hustone: /* Background */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each year on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies [http://en.wikipedia.org/wiki/Streptomyces ''Streptomyces''] is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig. 4''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:22:47Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a means to test and improve the individual parts of his or her system in a nonbiased manner as he or she tries to meet this optimal model.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:20:56Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a means to test and improve the individual parts of his or her system in a nonbiased manner as he or she tries to meet this optimal model.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''fully-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:10:11Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer constructs like Jay Keasling’s artemisinic acid-producing yeast through simple mutagenesis and selection; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. Synthetic biology can be used to construct complex biological systems, but these systems need testing to understand why they do not meet optimal models; the nonbiased improvements made through directed evolution represent an expedient way to do just such testing.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together interesting, new, and ''fully-functioning'' products.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:06:27Z

Hustone: /* Project Proposal */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer constructs like Jay Keasling’s artemisinic acid-producing yeast through simple mutagenesis and selection; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. Synthetic biology can be used to construct complex biological systems, but these systems need testing to understand why they do not meet optimal models; the nonbiased improvements made through directed evolution represent an expedient way to do just such testing.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together interesting, new, and ''fully-functioning'' products.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T21:06:05Z

Hustone: /* Project Proposal */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer constructs like Jay Keasling’s artemisinic acid-producing yeast through simple mutagenesis and selection; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. Synthetic biology can be used to construct complex biological systems, but these systems need testing to understand why they do not meet optimal models; the nonbiased improvements made through directed evolution represent an expedient way to do just such testing.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together interesting, new, and ''fully-functioning'' products.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:45:12Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer constructs like Jay Keasling’s artemisinic acid-producing yeast through simple mutagenesis and selection; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. Synthetic biology can be used to construct complex biological systems, but these systems need testing to understand why they do not meet optimal models; the nonbiased improvements made through directed evolution represent an expedient way to do just such testing.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together interesting, new, and ''fully-functioning'' products.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:44:27Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer constructs like Jay Keasling’s artemisinic acid-producing yeast through simple mutagenesis and selection; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. Synthetic biology can be used to construct complex biological systems, but these systems need testing to understand why they do not meet optimal models; the nonbiased improvements made through directed evolution represent an expedient way to do just such testing.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to form vastly improved products.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:27:29Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot hope to engineer constructs like Jay Keasling’s artemisinin-producing yeast through simple mutagenesis and selection. However, as shown in these four papers, the method is an effective way to test and improve existing systems at multiple levels. In addition, just like with experiments in synthetic biology, the end result of directed evolution informs us more about cellular function, whether it be protein structure or revealing unknown genes.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:27:17Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that directed evolution does not belong within the realm of synythetic biology of this lack of reasoning. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that methods in directed evolution are headed in a much more rational direction. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot hope to engineer constructs like Jay Keasling’s artemisinin-producing yeast through simple mutagenesis and selection. However, as shown in these four papers, the method is an effective way to test and improve existing systems at multiple levels. In addition, just like with experiments in synthetic biology, the end result of directed evolution informs us more about cellular function, whether it be protein structure or revealing unknown genes.

At the same time, how does the synthetic biologist make sure every part of his or her biological construct meets the optimal model? Directed evolution provides a method to test the effects multiple changes to the system in a nonbiased manner.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:14:58Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say their work does not belong in the realm of synthetic biology for this reason. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with the production of a useful product that meets a need? Research described in the four papers reviewed certainly meets this criterion.

Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that directed evolution has become a much more rational process. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot hope to create constructs like Jay Keasling’s artemisinin-producing yeast through simple mutagenesis and selection. At the same time, how does the synthetic biologist make sure every part of his or her biological construct meets the optimal model? Directed evolution provides a method to test the effects multiple changes to the system in a nonbiased manner.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T20:12:42Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall et al., Neuenschwander et al., and Alper et al. have all described the successful use of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say their work does not belong in the realm of synthetic biology for this reason. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with the production of a useful product that meets a need? Research described in the four papers reviewed certainly meets this criterion.

Furthermore, the work by Stuzman-Engwall et al. and Neuenschwander et el. indicates that directed evolution has become a much more rational process. Semi-synthetic DNA shuffling allows the researcher to control what mutations stay in wheel of directed evolution, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selection.

Perhaps the best strategy is to leave definitions aside. As Alper’s team showed in lycopene-producing E. coli, directed evolution can work in concert with the systematic modeling of synthetic biology to meet overaching goals like increases in production. The two methods appear well suited for one another. Directed evolution cannot hope to create constructs like Jay Keasling’s artemisinin-producing yeast through simple mutagenesis and selection. At the same time, how does the synthetic biologist make sure every part of his or her biological construct meets the optimal model? Directed evolution provides a method to test the effects multiple changes to the system in a nonbiased manner.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T19:28:55Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T18:37:13Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from whole cells. So long as improvements can be screened and selected for, these methods also appear applicable to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]], as well

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T18:23:02Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to changes in may different elements that make up a system, whether that system is a conventional enzymatic pathway or elaborate cell circuitry designed by humans (such as an array of [[Logic Gates - Emma Garren|Logic Gates]]). So long as improvements in the targeted construct can be screened and selected for, "genome-wide" directed evolution appears a promising way to optimize naturally-occurring and synthetic phenotypes.

The follow papers describe using "genome-wide" directed evolution to optimize product output of whole cells.

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T18:21:58Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to changes in may different elements that make up a system, whether that system is a conventional enzymatic pathway or elaborate cell circuitry designed by humans (such as an array of [Logic Gates - Emma Garren|Logic Gates]). So long as improvements in the targeted construct can be screened and selected for, "genome-wide" directed evolution appears a promising way to optimize naturally-occurring and synthetic phenotypes.

The follow papers describe using "genome-wide" directed evolution to optimize product output of whole cells.

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T18:21:27Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to changes in may different elements that make up a system, whether that system is a conventional enzymatic pathway or elaborate cell circuitry designed by humans (such as an array of [Logic Gates - Emma Garren | Logic Gates]). So long as improvements in the targeted construct can be screened and selected for, "genome-wide" directed evolution appears a promising way to optimize naturally-occurring and synthetic phenotypes.

The follow papers describe using "genome-wide" directed evolution to optimize product output of whole cells.

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T18:17:06Z

Hustone: /* "Genome-wide" Directed Evolution */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to changes in may different elements that make up a system, whether that system is a conventional enzymatic pathway or elaborate cell circuitry designed by humans (Biologic Gates). So long as improvements in the targeted construct can be screened and selected for, "genome-wide" directed evolution appears a promising way to optimize naturally-occurring and synthetic phenotypes.

The follow papers describe using "genome-wide" directed evolution to optimize product output of whole cells.

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-06T17:58:50Z

Hustone: /* Conclusion */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which has been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (e.g. a gene, a cell, etc.) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution could be invaluable to synthetic biology by providing methods to test how parts work together as well how they work in the cell.

The following two papers describe attempts to use "genome-wide" directed evolution to optimize organisms for a specific phenotype. In the first, Alper ''et al.'' use random gene knockouts in concert with model-predicted knockouts to optimize the output of a lycopene-producing strain of ''E. coli.'' In the second, the authors engineer a strain of yeast with high levels of ethanol production and resistance. This paper also describes a promising new method which allowed the team to circumvent rational modeling altogether in the creation of this strain of yeast.

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:58:14Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages of the Method==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

==Disadvantages of the Method==

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:58:05Z

Hustone: /* Advantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages of the Method==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

==Disadvantages==

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Global Transcriptome Machinery Engineering

2007-12-06T17:57:35Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages of the Method==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages of the Method==

# The primary limitation of gTME is that it can only test how changes to gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# A choice must be made about which transcription factor is subjected to random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Global Transcriptome Machinery Engineering

2007-12-06T17:57:24Z

Hustone: /* Advantages */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages of the Method==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes to gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# A choice must be made about which transcription factor is subjected to random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:56:33Z

Hustone: /* Disadvantages of the Method */

__NOTOC__

==Background==

Researchers Neunschwander ''et al.'' had been working with the enzyme chorismate mutase (EcCM), which is responsible for converting the metabolic intermediate chorismate to prephanate. The researchers wanted to introduce a “five-amino-acid hinge loop” in one of the enzyme’s helixes. However, this insertion drastically affected the enzyme’s capacity to catalyze the chorismate to prephanate reaction ('''Fig. 1''').

==The Experiment==

===Goals===

The team hoped to utilize directed evolution to restore enzymatic activity to the new form of the enzyme, hEcCM.

===Problems with Directed Evolution===

A mutant library of the hEcCM gene was generated using error-prone PCR and DNA shuffling and selection was run ''in vivo'' in ''E. coli'' auxotrophic for chorismate mutase. However, the hEcCM enzyme produced using this protocol, tEcCM, did not have an increase in enzymatic activity ('''Fig. 1''').

[[Image:Efficient_enzyme_data2.jpg]]

(Neunschwander ''et al.'', 2007 - Permission Pending)

'''Figure 1''' - Quantification of the catalytic activity of the wild-type chorismate mutase enzyme (EcCM), chorismate with the engineered helix-loop (hEcCM), the evolved enzyme after two rounds of directed evolution (tEcCM), and the evolved enzyme after directed evolution with "selectio" expression vector (EcCM-200/4 - see '''Fig. 2'''). Catalytic activity was determined by an ''in vitro'' enzyme specific assay.

===Finding a Solution===

The authors hypothesized that if they were able increase selective pressure for catalytic activity during the selection process, directed evolution would be much more effective at restoring the enzymatic activity of hEcCM.

To achieve their goal, the authors came up with the following solution: transform an auxtrophic strain of E. coli with an expression vector containing the hEcCM gene and devise a way to keep the enzyme at very low concentrations. Inefficient catalyst activity would result in lethality for the cell (a common selection scheme). Since the levels of the mutated hEcCM would be kept low, only the most efficient enzymes would survive through the selection scheme.

The researchers changed the expression of hEcCM in two ways to keep the enzyme at low concentrations. First, they inserted the hEcCM gene behind a Ptet promoter cassette, which allowed the team to control the cellular levels of the enzyme as a function of tetracycline concentration ('''Fig. 2'''). Secondly, they inserted an ssrA tag behind the enzyme, making the enzyme susceptible to degradation by the protease ClpXP ('''Fig. 2''').

[[Image:EFFICIENTENZYME.jpg]]

(Neuenschwander ''et al.'', 2007 - permission pending)

'''Figure 2''' - The constructed expression vector used to evovle the hEcCM gene. The combination of tetracycline-induced expression and constant degradation by the protease ClpXP meant the hEcCM enzyme was kept at constant low levels during selection, which increased selective pressure for higher rates of activity in the enzyme.

===Results===

To test the effectiveness of this new method, they created a mutated library of the tEcCM gene using error-prone PCR and DNA shuffling and inserted these genes into this new expression vector. These plasmids were then transformed into the auxotrophic strain of E. coli and the organisms were subjected to a single round of directed evolution. The most efficient mutant of this experiment, EcCM-200/4,
displayed a level of enzymatic activity comparable to the original wild-type chorismate mutase gene ('''Fig. 1''').

==Advantages of the Method==

# The method Neuenschwander et al. proved useful and restoring enzymatic activity to their synthetically-engineered catalyst.
# The "selection vector" used in this experiment is modular: when selective pressure is too low to improve proteins in other experiments with directed evolution, the "selection vector" can be used to increase selective pressure on the protein for phenotype improvement.

==Disadvantages of the Method==

# The technique requires the use of auxotropihc organisms to produce the selected pressure required to drive the improvement in the protein of interest.
# The protein of interest must be essential to the life of the organism which may not be the case with completely novel proteins engineered through synthetic biology.
# The method requires the knowledge of gene function, which often is not the case.
# This method generates a large number false positives due to some ''E. coli'' receiving two or more copies of the expression plasmid.

==Source==

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:55:52Z

Hustone: /* Conclusion */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each year on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig. 4''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:55:30Z

Hustone: /* Sources */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each year on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig.''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:54:58Z

Hustone: /* Sources */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each year on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig.''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:54:03Z

Hustone: /* Background */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each year on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig.''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:53:38Z

Hustone: /* Conclusion */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig.''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

===Sources===

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:50:57Z

Hustone: /* Advantages of the Method */

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:48:05Z

Hustone: /* Goals */

__NOTOC__

==Background==

Researchers Neunschwander ''et al.'' had been working with the enzyme chorismate mutase (EcCM), which is responsible for converting the metabolic intermediate chorismate to prephanate. The researchers wanted to introduce a “five-amino-acid hinge loop” in one of the enzyme’s helixes. However, this insertion drastically affected the enzyme’s capacity to catalyze the chorismate to prephanate reaction ('''Fig. 1''').

==The Experiment==

===Goals===

The team hoped to utilize directed evolution to restore enzymatic activity to the new form of the enzyme, hEcCM.

===Problems with Directed Evolution===

A mutant library of the hEcCM gene was generated using error-prone PCR and DNA shuffling and selection was run ''in vivo'' in ''E. coli'' auxotrophic for chorismate mutase. However, the hEcCM enzyme produced using this protocol, tEcCM, did not have an increase in enzymatic activity ('''Fig. 1''').

[[Image:Efficient_enzyme_data2.jpg]]

(Neunschwander ''et al.'', 2007 - Permission Pending)

'''Figure 1''' - Quantification of the catalytic activity of the wild-type chorismate mutase enzyme (EcCM), chorismate with the engineered helix-loop (hEcCM), the evolved enzyme after two rounds of directed evolution (tEcCM), and the evolved enzyme after directed evolution with "selectio" expression vector (EcCM-200/4 - see '''Fig. 2'''). Catalytic activity was determined by an ''in vitro'' enzyme specific assay.

===Finding a Solution===

The authors hypothesized that if they were able increase selective pressure for catalytic activity during the selection process, directed evolution would be much more effective at restoring the enzymatic activity of hEcCM.

To achieve their goal, the authors came up with the following solution: transform an auxtrophic strain of E. coli with an expression vector containing the hEcCM gene and devise a way to keep the enzyme at very low concentrations. Inefficient catalyst activity would result in lethality for the cell (a common selection scheme). Since the levels of the mutated hEcCM would be kept low, only the most efficient enzymes would survive through the selection scheme.

The researchers changed the expression of hEcCM in two ways to keep the enzyme at low concentrations. First, they inserted the hEcCM gene behind a Ptet promoter cassette, which allowed the team to control the cellular levels of the enzyme as a function of tetracycline concentration ('''Fig. 2'''). Secondly, they inserted an ssrA tag behind the enzyme, making the enzyme susceptible to degradation by the protease ClpXP ('''Fig. 2''').

[[Image:EFFICIENTENZYME.jpg]]

(Neuenschwander ''et al.'', 2007 - permission pending)

'''Figure 2''' - The constructed expression vector used to evovle the hEcCM gene. The combination of tetracycline-induced expression and constant degradation by the protease ClpXP meant the hEcCM enzyme was kept at constant low levels during selection, which increased selective pressure for higher rates of activity in the enzyme.

===Results===

To test the effectiveness of this new method, they created a mutated library of the tEcCM gene using error-prone PCR and DNA shuffling and inserted these genes into this new expression vector. These plasmids were then transformed into the auxotrophic strain of E. coli and the organisms were subjected to a single round of directed evolution. The most efficient mutant of this experiment, EcCM-200/4,
displayed a level of enzymatic activity comparable to the original wild-type chorismate mutase gene ('''Fig. 1''').

==Advantages of the Method==

The method Neuenschwander et al. proved useful and restoring enzymating efficiency to their synthetically-engineered catalyst.

Modularity – change the concentration of the protein depending on the protein. Can easily swap out proteins on their plasmid.

This method appears to be very useful for improving individuals proteins.

==Disadvantages of the Method==

# The technique requires the use of auxotropihc organisms to produce the selected pressure required to drive the improvement in the protein of interest.
# The protein of interest must be essential to the life of the organism which may not be the case with completely novel proteins engineered through synthetic biology.
# The method requires the knowledge of gene function, which often is not the case.
# This method generates a large number false positives due to some ''E. coli'' receiving two or more copies of the expression plasmid.

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:47:55Z

Hustone: /* The Experiment */

__NOTOC__

==Background==

Researchers Neunschwander ''et al.'' had been working with the enzyme chorismate mutase (EcCM), which is responsible for converting the metabolic intermediate chorismate to prephanate. The researchers wanted to introduce a “five-amino-acid hinge loop” in one of the enzyme’s helixes. However, this insertion drastically affected the enzyme’s capacity to catalyze the chorismate to prephanate reaction ('''Fig. 1''').

==The Experiment==

===Goals===

The team hoped to utilize directed evolution to restore enzymatic activity to the new form of the enzyme, hEcCM.

===Problems with Directed Evolution===

A mutant library of the hEcCM gene was generated using error-prone PCR and DNA shuffling and selection was run ''in vivo'' in ''E. coli'' auxotrophic for chorismate mutase. However, the hEcCM enzyme produced using this protocol, tEcCM, did not have an increase in enzymatic activity ('''Fig. 1''').

[[Image:Efficient_enzyme_data2.jpg]]

(Neunschwander ''et al.'', 2007 - Permission Pending)

'''Figure 1''' - Quantification of the catalytic activity of the wild-type chorismate mutase enzyme (EcCM), chorismate with the engineered helix-loop (hEcCM), the evolved enzyme after two rounds of directed evolution (tEcCM), and the evolved enzyme after directed evolution with "selectio" expression vector (EcCM-200/4 - see '''Fig. 2'''). Catalytic activity was determined by an ''in vitro'' enzyme specific assay.

===Finding a Solution===

The authors hypothesized that if they were able increase selective pressure for catalytic activity during the selection process, directed evolution would be much more effective at restoring the enzymatic activity of hEcCM.

To achieve their goal, the authors came up with the following solution: transform an auxtrophic strain of E. coli with an expression vector containing the hEcCM gene and devise a way to keep the enzyme at very low concentrations. Inefficient catalyst activity would result in lethality for the cell (a common selection scheme). Since the levels of the mutated hEcCM would be kept low, only the most efficient enzymes would survive through the selection scheme.

The researchers changed the expression of hEcCM in two ways to keep the enzyme at low concentrations. First, they inserted the hEcCM gene behind a Ptet promoter cassette, which allowed the team to control the cellular levels of the enzyme as a function of tetracycline concentration ('''Fig. 2'''). Secondly, they inserted an ssrA tag behind the enzyme, making the enzyme susceptible to degradation by the protease ClpXP ('''Fig. 2''').

[[Image:EFFICIENTENZYME.jpg]]

(Neuenschwander ''et al.'', 2007 - permission pending)

'''Figure 2''' - The constructed expression vector used to evovle the hEcCM gene. The combination of tetracycline-induced expression and constant degradation by the protease ClpXP meant the hEcCM enzyme was kept at constant low levels during selection, which increased selective pressure for higher rates of activity in the enzyme.

===Results===

To test the effectiveness of this new method, they created a mutated library of the tEcCM gene using error-prone PCR and DNA shuffling and inserted these genes into this new expression vector. These plasmids were then transformed into the auxotrophic strain of E. coli and the organisms were subjected to a single round of directed evolution. The most efficient mutant of this experiment, EcCM-200/4,
displayed a level of enzymatic activity comparable to the original wild-type chorismate mutase gene ('''Fig. 1''').

==Advantages of the Method==

The method Neuenschwander et al. proved useful and restoring enzymating efficiency to their synthetically-engineered catalyst.

Modularity – change the concentration of the protein depending on the protein. Can easily swap out proteins on their plasmid.

This method appears to be very useful for improving individuals proteins.

==Disadvantages of the Method==

# The technique requires the use of auxotropihc organisms to produce the selected pressure required to drive the improvement in the protein of interest.
# The protein of interest must be essential to the life of the organism which may not be the case with completely novel proteins engineered through synthetic biology.
# The method requires the knowledge of gene function, which often is not the case.
# This method generates a large number false positives due to some ''E. coli'' receiving two or more copies of the expression plasmid.

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:47:41Z

Hustone: /* Goals */

__NOTOC__

==Background==

Researchers Neunschwander ''et al.'' had been working with the enzyme chorismate mutase (EcCM), which is responsible for converting the metabolic intermediate chorismate to prephanate. The researchers wanted to introduce a “five-amino-acid hinge loop” in one of the enzyme’s helixes. However, this insertion drastically affected the enzyme’s capacity to catalyze the chorismate to prephanate reaction ('''Fig. 1''').

==The Experiment==

===Goals===

The team hoped to utilize directed evolution to restore enzymatic activity to the new form of the enzyme, hEcCM.

==The Experiment==

===Problems with Directed Evolution===

A mutant library of the hEcCM gene was generated using error-prone PCR and DNA shuffling and selection was run ''in vivo'' in ''E. coli'' auxotrophic for chorismate mutase. However, the hEcCM enzyme produced using this protocol, tEcCM, did not have an increase in enzymatic activity ('''Fig. 1''').

[[Image:Efficient_enzyme_data2.jpg]]

(Neunschwander ''et al.'', 2007 - Permission Pending)

'''Figure 1''' - Quantification of the catalytic activity of the wild-type chorismate mutase enzyme (EcCM), chorismate with the engineered helix-loop (hEcCM), the evolved enzyme after two rounds of directed evolution (tEcCM), and the evolved enzyme after directed evolution with "selectio" expression vector (EcCM-200/4 - see '''Fig. 2'''). Catalytic activity was determined by an ''in vitro'' enzyme specific assay.

===Finding a Solution===

The authors hypothesized that if they were able increase selective pressure for catalytic activity during the selection process, directed evolution would be much more effective at restoring the enzymatic activity of hEcCM.

To achieve their goal, the authors came up with the following solution: transform an auxtrophic strain of E. coli with an expression vector containing the hEcCM gene and devise a way to keep the enzyme at very low concentrations. Inefficient catalyst activity would result in lethality for the cell (a common selection scheme). Since the levels of the mutated hEcCM would be kept low, only the most efficient enzymes would survive through the selection scheme.

The researchers changed the expression of hEcCM in two ways to keep the enzyme at low concentrations. First, they inserted the hEcCM gene behind a Ptet promoter cassette, which allowed the team to control the cellular levels of the enzyme as a function of tetracycline concentration ('''Fig. 2'''). Secondly, they inserted an ssrA tag behind the enzyme, making the enzyme susceptible to degradation by the protease ClpXP ('''Fig. 2''').

[[Image:EFFICIENTENZYME.jpg]]

(Neuenschwander ''et al.'', 2007 - permission pending)

'''Figure 2''' - The constructed expression vector used to evovle the hEcCM gene. The combination of tetracycline-induced expression and constant degradation by the protease ClpXP meant the hEcCM enzyme was kept at constant low levels during selection, which increased selective pressure for higher rates of activity in the enzyme.

===Results===

To test the effectiveness of this new method, they created a mutated library of the tEcCM gene using error-prone PCR and DNA shuffling and inserted these genes into this new expression vector. These plasmids were then transformed into the auxotrophic strain of E. coli and the organisms were subjected to a single round of directed evolution. The most efficient mutant of this experiment, EcCM-200/4,
displayed a level of enzymatic activity comparable to the original wild-type chorismate mutase gene ('''Fig. 1''').

==Advantages of the Method==

The method Neuenschwander et al. proved useful and restoring enzymating efficiency to their synthetically-engineered catalyst.

Modularity – change the concentration of the protein depending on the protein. Can easily swap out proteins on their plasmid.

This method appears to be very useful for improving individuals proteins.

==Disadvantages of the Method==

# The technique requires the use of auxotropihc organisms to produce the selected pressure required to drive the improvement in the protein of interest.
# The protein of interest must be essential to the life of the organism which may not be the case with completely novel proteins engineered through synthetic biology.
# The method requires the knowledge of gene function, which often is not the case.
# This method generates a large number false positives due to some ''E. coli'' receiving two or more copies of the expression plasmid.

A Simple Method for Highly Evolved Enzymes

2007-12-06T17:47:07Z

Hustone: /* Background */

__NOTOC__

==Background==

Researchers Neunschwander ''et al.'' had been working with the enzyme chorismate mutase (EcCM), which is responsible for converting the metabolic intermediate chorismate to prephanate. The researchers wanted to introduce a “five-amino-acid hinge loop” in one of the enzyme’s helixes. However, this insertion drastically affected the enzyme’s capacity to catalyze the chorismate to prephanate reaction ('''Fig. 1''').

==Goals==

The team hoped to utilize directed evolution to restore enzymatic activity to the new form of the enzyme, hEcCM.

==The Experiment==

===Problems with Directed Evolution===

A mutant library of the hEcCM gene was generated using error-prone PCR and DNA shuffling and selection was run ''in vivo'' in ''E. coli'' auxotrophic for chorismate mutase. However, the hEcCM enzyme produced using this protocol, tEcCM, did not have an increase in enzymatic activity ('''Fig. 1''').

[[Image:Efficient_enzyme_data2.jpg]]

(Neunschwander ''et al.'', 2007 - Permission Pending)

'''Figure 1''' - Quantification of the catalytic activity of the wild-type chorismate mutase enzyme (EcCM), chorismate with the engineered helix-loop (hEcCM), the evolved enzyme after two rounds of directed evolution (tEcCM), and the evolved enzyme after directed evolution with "selectio" expression vector (EcCM-200/4 - see '''Fig. 2'''). Catalytic activity was determined by an ''in vitro'' enzyme specific assay.

===Finding a Solution===

The authors hypothesized that if they were able increase selective pressure for catalytic activity during the selection process, directed evolution would be much more effective at restoring the enzymatic activity of hEcCM.

To achieve their goal, the authors came up with the following solution: transform an auxtrophic strain of E. coli with an expression vector containing the hEcCM gene and devise a way to keep the enzyme at very low concentrations. Inefficient catalyst activity would result in lethality for the cell (a common selection scheme). Since the levels of the mutated hEcCM would be kept low, only the most efficient enzymes would survive through the selection scheme.

The researchers changed the expression of hEcCM in two ways to keep the enzyme at low concentrations. First, they inserted the hEcCM gene behind a Ptet promoter cassette, which allowed the team to control the cellular levels of the enzyme as a function of tetracycline concentration ('''Fig. 2'''). Secondly, they inserted an ssrA tag behind the enzyme, making the enzyme susceptible to degradation by the protease ClpXP ('''Fig. 2''').

[[Image:EFFICIENTENZYME.jpg]]

(Neuenschwander ''et al.'', 2007 - permission pending)

'''Figure 2''' - The constructed expression vector used to evovle the hEcCM gene. The combination of tetracycline-induced expression and constant degradation by the protease ClpXP meant the hEcCM enzyme was kept at constant low levels during selection, which increased selective pressure for higher rates of activity in the enzyme.

===Results===

To test the effectiveness of this new method, they created a mutated library of the tEcCM gene using error-prone PCR and DNA shuffling and inserted these genes into this new expression vector. These plasmids were then transformed into the auxotrophic strain of E. coli and the organisms were subjected to a single round of directed evolution. The most efficient mutant of this experiment, EcCM-200/4,
displayed a level of enzymatic activity comparable to the original wild-type chorismate mutase gene ('''Fig. 1''').

==Advantages of the Method==

The method Neuenschwander et al. proved useful and restoring enzymating efficiency to their synthetically-engineered catalyst.

Modularity – change the concentration of the protein depending on the protein. Can easily swap out proteins on their plasmid.

This method appears to be very useful for improving individuals proteins.

==Disadvantages of the Method==

# The technique requires the use of auxotropihc organisms to produce the selected pressure required to drive the improvement in the protein of interest.
# The protein of interest must be essential to the life of the organism which may not be the case with completely novel proteins engineered through synthetic biology.
# The method requires the knowledge of gene function, which often is not the case.
# This method generates a large number false positives due to some ''E. coli'' receiving two or more copies of the expression plasmid.

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:46:44Z

Hustone: /* Conclusion */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene. The method also appears adept for recombining beneficial mutations when using directed evolution to improve genes ('''Fig.''') and allows for some degree of rational input into directed evolution through the use of oligonucleotides in shuffling.

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:42:09Z

Hustone: /* In the Lab */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

===In the Lab===

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene.

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:41:59Z

Hustone: /* The Experiment */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

==In the Lab==

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene.

Semi-Synthetic DNA Shuffling and Doramectin

2007-12-06T17:41:45Z

Hustone: /* The Goal */

__NOTOC__

==Background==

Doramectin is a drug used to treat gastrointestinal roundworms, lungworms, eyeworms, grubs, and sucking lice in cattle. The drug is one of several avermectins, compounds used for the treatment of parasites in animals and river blindness in humans. Over 1 billion dollars is spent each on avermectin derivatives in the United States.

Avermectins are produced by the soil-borne bacteria ''Streptomyces avermitilis.'' The subspecies ''Streptomyces'' is the largest genus of antibiotic-producing bacteria; erythromycin, neomycin, streptomycin, and tetracycline were all originally derived from species of Streptomyces.

==The Experiment==

===The Goal===

Researchers Stutzman-Engwall ''et al.'' began their experiment with a strain of ''S. avermitilis'' capable of producing doramectin from supplemented cyclohexancaroxylic acid. This strain produced doramectin in two forms: CHC-B1, the most useful form of doramectin, and CHC-B2, a related compound less effective as an antiparasital than CHC-B1. The ratio of ineffective CHC-B2 to effective CHC-B1 produced by this strain was 1:1.

The researchers sought to engineer a strain of ''S. avermitilis'' that would produce higher yields of the B1 form of doramectin. The team had already identified that the gene ''aveC'' was responsible for the B2:B1 ratio. This knowledge led the team to conduct directed evolution upon the ''aveC'' gene.

==The Experiment==

The researchers conducted three rounds of directed evolution on the ''aveC'' gene. To generate a mutant library of the gene, the researchers used the mutagenic agent, Mutazyme, and semi-synthetic DNA shuffling (see following section). The mutant ''aveC'' genes were inserted into gene replacement vectors, which were then transformed into ''S. avermitilis'' cells. These cells were diluted into 96 well plates for high throughput culturing under conditions suitable for doramectin production. Selection of the best mutants was conducted by testing the doramectin output and B2:B1 ratios of each well (quantifiable with [http://en.wikipedia.org/wiki/MS/MS MS/MS]). DNA isolated was isolated from the best mutants and the ''aveC'' gene was amplified using PCR. The amplified DNA from these mutants was recombined using semi-synthetic shuffling, subjected to random mutagenesis, and resubmitted to selection.

After three rounds of directed evolution, the resulting strains of ''S. avermitilis'' produced doramectin with a B2 to B1 ratio of 0.07:1 ('''Fig. 1'''); This ratio was significantly lower than the ratio in wild-type strain of ''S. avermitilis'' (1:1).

[[Image:DORAMECTIN_FIGURE2.jpg]]

(Stutzman-Engwall ''et al.'', 2005 - Permission Pending)

'''Figure 1''' - Doramectin production profiles of thirteen strains of ''S. avermitilis'' transformed with the most evolved form of the ''aveC'' gene after three rounds of directed evolution. Numbers above each bar represent ratios of CHC-B2 to doramectin produced by the strain. Fermentation analysis for each strain was conducted in a 30 mL shake flask under humidity for 12-14 days. Each strain in which wild-type ''aveC'' had been replaced by the evolved ''aveC'' displayed a significant reduction in the B2:B1 (B2:Doramectin) ratio.

==Semi-synthetic DNA Shuffling==

During the process of genetic randomization in this experiments, the researchers used a technique termed DNA shuffling. This technique, sometimes called sexual PCR, involves artificially recombining the genes of the best mutants at the end of each round of selection. Through these recombinations, beneficial mutations which are lost during the selection process are recaptured ('''Fig. 2''').

[[Image:DNAshufflingtheorysmall.jpg]]

'''Figure 2''' - Unlike directed evolution alone, directed evolution with DNA shuffling reinserts the best mutations of a ''generation'' rather than simply an ''individual.''

The first step of DNA shuffling involves digesting the gene of interest with DNAse1 into fragments. Primerless PCR is then conducted on these fragments; under these conditions, ''Taq'' polymerase is able to reassemble the fragmented gene. Hopefully, during this fragmenting and reassembling of the gene of interest, different mutations proven beneficial by selection during directed evolution are recombined into a single gene ('''Fig. 3''').

[[Image:Stemmer.jpg]]

(Stemmer 1994 - Permission Pending)

'''Figure 3''' - DNA shuffling uncoupling beneficial mutations (represented by X's) and recombining them through gene reassebmly. Product represents the theoretical best output of DNA shuffling.

The work by Stutzman-Engwall ''et al.'' represents a further development on the technique of DNA shuffling. They have termed their new method "semi-synthetic DNA shuffling." This method followed the same protocol as DNA shuffling described above, except that all beneficial mutations of the ''aveC'' gene were stored as oligonucleotides. By this method, beneficial mutations can be continuously reintroduced by inserting these oligonucleotides in high molar concentrations during DNA shuffling. The researchers hypothesized this new development in DNA shuffling would prevent beneficial mutations from being lost. Furthermore, this technique allowed the team to introduce four mutations previously demonstrated to lower the B2:B1 doramectin ratio into the shuffling scheme.

Although we are unable to tell how their new technique compared to directed evolution with normal DNA shuffling or directed evolution without any DNA shuffling altogether, there is a clear correlation between enrichment in mutations and improvement of phenotype as successive rounds of semi-synthetic DNA shuffling were completed ('''Fig. 4''').

[[Image:DORAMECTIN2.jpg]]

(Stutzman-Engwall ''et al.'', 2005)

'''Figure 4''' - The ratio of CHC-B2:CHCB1 vs. the number of amino acid substitutions in the clone compared to the wild type. The first square represents wild-type ''S. avermitilis'' and the second square represents the production strain of ''S. avermitilis'' subjected to directed evolution. Triangles represent the best clones from the first round; Xs represent te best clones from the second round; diamonds represent best clones of the third round. Each round of directed evolution using semi-synthetic shuffling shows both an increase in doramectin production phenotype and number of mutations in the evolved clone.

==Conclusion==

Semi-synthetic shuffling proved successful in quickly and efficiently improving the function of the ''aveC'' gene.

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:40:32Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

==Disadvantages==

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:40:23Z

Hustone: /* Advantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

===Disadvantages===

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:38:58Z

Hustone: /* Computer Modeling Versus Random Gene Knockout */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

===Advantages===

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

===Disadvantages===

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:38:34Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing (Fig. 1a). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture (Fig. 1b).

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

===Advantages===

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

===Disadvantages===

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:33:17Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing (Fig. 1a). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture (Fig. 1b).

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

===Advantages===

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

===Disadvantages===

# In this experiment, random transposon intergration could test how gene knockouts increased lycopene production. The method of directed evolution the researchers used cannot test changes in gene expression levels or genes codes to optimize phenotypes.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Global Transcriptome Machinery Engineering

2007-12-06T17:27:09Z

Hustone: /* Disadvantages */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes to gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# A choice must be made about which transcription factor is subjected to random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Global Transcriptome Machinery Engineering

2007-12-06T17:25:21Z

Hustone: /* Advantages */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# There appears to be no clear way to choose which transcription factor is altered to during random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Global Transcriptome Machinery Engineering

2007-12-06T17:24:20Z

Hustone: /* In the Lab */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method method was able to meet these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates further possibilities for basic research about the complex phenotype being selected for. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# There appears to be no clear way to choose which transcription factor is altered to during random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Global Transcriptome Machinery Engineering

2007-12-06T17:21:48Z

Hustone: /* Using gTME to Engineer a Complex Phenotype */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.

To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.

The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.

The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted in expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function result from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose (6% ethanol, 120 g/L glucose). The mutant that showed the most growth after one round of this selection, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant to quantify its increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method method was able to meet these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates further possibilities for basic research about the complex phenotype being selected for. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# There appears to be no clear way to choose which transcription factor is altered to during random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Global Transcriptome Machinery Engineering

2007-12-06T17:12:50Z

Hustone: /* Goals */

__NOTOC__

==The Experiment==

===Goals===

Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.

===Using gTME to Engineer a Complex Phenotype===

The ethanol resistance and production phenotypes are regulated by many different genes in the yeast cell, and most of these genes are unknown. This fundamental lack of knowledge meant that the researchers could not rationally engineer the desired phenotype.

To circumvent this problem, the researchers describe a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering, is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution used for individual proteins, this method also does not require one have gene to function knowledge before they begin directed evolution.

The process is almost exactly the same as the forms of directed evolution described in previous papers, except that instead of using random mutagenesis to create a mutant library of a single gene, mutagenesis is used to create a mutant library of a selected transcription factor. gTME works under the concept that each mutant transcription factor will drastically change how genes are expressed across the entire cell once it has been transferred ''in vivo.'' The phenotypes created by these new changed expression levels in cell can then be run through selection to select for the desired phenotype (in this case, ethanol production and resistance).

===In the Lab===

After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted in expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function result from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose (6% ethanol, 120 g/L glucose). The mutant that showed the most growth after one round of this selection, ''spt15-300,'' was isolated.

The team ran tests on the ''spt15-300'' mutant to quantify its increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.)

[[Image:FIGURE ETHANOL.jpg]]

(Alper ''et al.'', 2006 - Permission Pending)

'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L

==Advantages==

# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
# The method method was able to meet these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
# Hypothetically, using the gTME method creates further possibilities for basic research about the complex phenotype being selected for. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.

==Disadvantages==

# The primary limitation of gTME is that it can only test how changes gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
# There appears to be no clear way to choose which transcription factor is altered to during random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.

==Sources==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

Random Gene Knockout to Maximize Product Yield

2007-12-06T17:11:43Z

Hustone: /* Source */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing (Fig. 1a). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture (Fig. 1b).

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

===Advantages===

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

===Disadvantages===

# By using transposons to create the mutant library for directed evolution, lycopene production could only be improved by gene knockout. This method does not involve ways to make changes to the genes themselves or change their expression in a fine tuned manner in order to optimize lycopene output.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]