Synthetic Biology Network Research
This page is designed as a community page for students at MWSU and Davidson College who are using synthetic biology to learn more about graph theory and network topology.
Our first meeting will be on Thursday, January 19, 2012. We will meet at 11 am (the common hour) in the Think Tank in the back of Belk computer lab.
- Weekly Journals (your own paper summary and those of others) = 30% final grade
- Weekly Presentations = 30% final grade
- Research Proposal by teams = 40% final grade
You must keep hard copies of your weekly journal entries in a 3-ring binder. We will grade these periodically during the semester. You will also keep copies of your papers, any drawings of ideas you have, protocols used in lab, etc.
Scheduling with Doodle
WEEK FOUR (February 6 - 10) WET LAB WEEK
WEEK EIGHT (March 5 - 9) SPRING BREAK
WEEK FOURTEEN (April 16 - 20)
Genomics Seminar 4:30 pm April 18 in Dana 146 and lunch with speaker Dr. Sallie Permar PhD, MD (& Davidson alumna) at Duke University
WEEK SEVENTEEN (May 7 - 9) READING DAY May 10
Over the next 14 weeks, we will read a series of papers. We have chosen some to help us get started, but as the semester progresses, you will take the lead in identifying papers. Some of these papers will be easy for you, but others will be more difficult. We will work as a group to understand what is going on. In all cases, we will use these papers to help us frame a research project that will be conducted this summer by 8 Davidson students.
We will need to become experts in the magnetosome produced by bacteria. We will need to identify key papers to understand what is known so far. We also need to understand what UW-Seattle iGEM2011 did with this project.
- The creativity crisis.
Po Bronson and Ashley Merryman
Newsweek. July 19, 2010. page 44.
- Synthetic Biology Moving into the Clinic
Warren C. Ruder,* Ting Lu,* James J. Collins
Science. Vol. 333. page 1248.
- Engineering bacteria to solve the Burnt Pancake Problem.
Haynes, Karmella, et al.
Journal of Biological Engineering. Vol. 2(8): 1 – 12.
- Solving a Hamiltonian Path Problem with a Bacterial Computer.
Baumgardner, Jordan et al.
Journal of Biological Engineering. Vol. 3:11
- Bacterial Hash Function Using DNA-Based XOR Logic Reveals Unexpected Behavior of the LuxR Promoter.
Brianna Pearson*, Kin H. Lau* et al.
Interdisciplinary Bio Central. Vol. 3, article no. 10
- DNA assembly for synthetic biology: from parts to pathways and beyond
Tom Ellis,*ab Tom Adieac and Geoff S. Baldwin
Integr. Biol., 2011, 3, 109–118
- Information Transduction Capacity of Noisy Biochemical Signaling Networks
Raymond Cheong, Alex Rhee, Chiaochun Joanne Wang, Ilya Nemenman, Andre Levchenko
Science. Vol. 334, page 354.
- Synthetic Biology: Regulating Industry Uses of New Biotechnologies
Brent Erickson, Rina Singh, Paul Winters
Science. Vol. 333, page 1254.
- Synthetic Biology: Integrated Gene Circuits
Nagarajan Nandagopal and Michael B. Elowitz
Science. Vol. 333, page 1244.
- Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Peter J. Mucha, Thomas Richardson, Kevin Macon, Mason A. Porter, and Jukka-Pekka Onnela
Science. Vol. 328. page 876-878.
- Stochastic Pulse Regulation in Bacterial Stress Response
James C. W. Locke,* Jonathan W. Young,* Michelle Fontes, María Jesús Hernández Jiménez, Michael B. Elowitz
Science. Vol. 334. page 366.
- Synthetic biology: applications come of age
Ahmad S. Khalil* and James J. Collins
Nature Review Genetics. Vol. 11. page 367.
- A Cultured Greigite-Producing Magnetotactic Bacterium in a Novel Group of Sulfate-Reducing Bacteria
Christopher T. Lefèvre, et al.
Science. Vol. 334. page 1720.
- Five hard truths for synthetic biology.
Nature. Vol. 463. page 288.
- Controllability of complex networks
Yang-Yu Liu1,2, Jean-Jacques Slotine3,4 & Albert-La ́szlo ́ Baraba ́si
Nature. Vol. 473. page 167.
"Networks and confusion"
I have been attempting to work through the current tentative idea for our approach to network projects. I was originally confused by the idea that we would optimize an entire genome via varying RBC's, promoters, degradation tags, etc, and that we could call that a network. To make sense of the general idea of a network that we could design and eventually "train" to function, I went back to the start of what makes a network a network.
A network is a specified pathway that via interaction with a stimulus produces/degrades/builds/reacts. Antibiotic resistance is a reaction to a stimulus, but generally only uses one gene to create resistance. Whereas a metabolic pathway can use a small number to many different "nodes" or parts to the system to produce a single overall reaction in response to the stimulus.
I had an idea, this is a generalized idea, is in no way complete, but should be used as the jumping-off point. It is this: Evolution, via phenotype variation, has created the myriad of reporters and selection tools we use currently in our labs to determine if our bacteria are behaving the way we want them to. In the evolution of the various types of antibiotics we have discovered and utilize to kill bacterium, so too have those bacteria evolved to resist these killers. Looking at the scope of the interaction that a bacterium community would have to undergo in order to build a resistance to an antibiotic, one can see that it isn't likely a quick thing. however, may antibiotics are similar in function, and the resistance we encode many of our projects with are similar in their DNA construct and function as well.
Generalized proposal using antibiotic resistance as the stand-in for network function:Italic text
Using a bacterial strain resistant to a specific antibiotic, we add the "tools" for said bacteria to become resistant to a different but similar antibiotic by chopping up the DNA that would give them resistance and adding them to the genome of the aforementioned bacteria. Basically, we would be smashing and then giving a Rubik's cube to blind bacteria, and ordering them to put it together or we allow the new antibiotic to shoot them. We then politely poke and prod them to get to work by reassembling, chopping, and trying out new ways to use these pieces. Along the way, we use the phenotypic variation (which is inherent in all organisms) of these bacteria to train some of them (hopefully the ones that at least got part of the puzzle correct) to reach the end of a "stepwise" pathway and put the Rubik's cube back together. This pathway, keeping in mind the phenotype variation of these bacteria, doesn't have to be A-B-C-END. They might start with small a, or a triangle that strongly resembles an A, but still something similar enough to deliver the end result: resistance to a new antibiotic. This will almost assuredly be a poly-divergent pathway from start to finish. Much like many people are tall and many are small, but some of the small and the tall have dark hair or light hair, we would use the variation of our bacteria to get them from ~A through to the END. It wouldn't matter how they got to the end, even if they take shortcuts, as long as they get there, all they have to do is tell us how they did it.
Long and short, we give them the tools to solve a problem or to build a pathway, and see if they can get it right. At this stage, there are no restrictions on this idea as it is still in its infancy.
Automated Design of Synthetic RBS's to Control Protein Expression: Media:Automated_Design_of_Synthetic_RBS's_to_Control_Protein_Expression.pdf