Origins and Characterization of Stochasticity - Revision history

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Miwaters at 17:26, 6 December 2007

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2007-12-06T14:29:41Z

Miwaters at 14:26, 6 December 2007

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Miwaters at 19:11, 5 December 2007

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Miwaters at 18:59, 5 December 2007

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Miwaters at 18:59, 5 December 2007

2007-12-05T18:59:21Z

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 | Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degradation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant sources from negligible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
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-A Prokaryotic vs. a Eukaryotic background is an example of an important distinction to make when characterizing stochasticity. Differences in transcriptional and translational processes lead to differences in dominant manifestations of non-genetic identity. In prokaryotes Ozbudak et. al. characterized a modular model of expression noise in prokaryotic Bacilus Subtillis. In his model stochastic variables representing transcriptional and translational processes represented the manifestation of stochasticity in the system. JJ Collins et. al. suggested that promoter kinetics, and not simply protein abundance, was a factor depending on if the chassis was prokaryotic or eukaryotic. The abundance of characterizations of noise in different backgrounds poses an obstacle for comparison and suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say however that that stochastic events cannot be predicted and taken into account. Check out the In Depth section of this page to find out about the underlying motifs in stochastic processes across different backgrounds.
+A Prokaryotic vs. a Eukaryotic background is an example of an important distinction to make when characterizing stochasticity. Differences in transcriptional and translational processes lead to differences in dominant manifestations of non-genetic identity. In prokaryotes Ozbudak ''et. al''. characterized a modular model of expression noise in prokaryotic ''Bacilus subtillis''. In his model, stochastic variables representing transcriptional and translational processes represented the manifestation of stochasticity in the system. JJ Collins ''et. al''. suggested that promoter kinetics, and not simply protein abundance, was a factor depending on if the chassis was prokaryotic or eukaryotic. The abundance of characterizations of noise in different backgrounds poses an obstacle for comparison and suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say however that that stochastic events cannot be predicted and taken into account. Check out the "In Depth" section of this page to find out about the underlying motifs in stochastic processes across different backgrounds.

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-<center>[[Term Paper Wiki| <span style="color:red">Home</span>]] | [[Origins and Characterization of Stochasticity| <span style="color:red">Origins and Characterization of Stochasticity</span>]] | [[Modeling Stochasticity| <span style="color:red">Modeling Stochasticity</span>]] | [[Manipulation of Stochasticity| <span style="color:red">Manipulation of Stochasticity</span>]] | [[Evolved Stochasticity? | <span style="color:red">Evolved Stochasticity?</span>]] |  [[Concluding Remarks| <span style="color:red">Concluding Remarks </span> ]] | [[Citations|<span style="color:red">Citations</span>]]</center>
+<center>[[Term_paper_wiki| <span style="color:red">Home</span>]] | [[Origins and Characterization of Stochasticity| <span style="color:red">Origins and Characterization of Stochasticity</span>]] | [[Modeling Stochasticity| <span style="color:red">Modeling Stochasticity</span>]] | [[Manipulation of Stochasticity| <span style="color:red">Manipulation of Stochasticity</span>]] | [[Evolved Stochasticity? | <span style="color:red">Evolved Stochasticity?</span>]] |  [[Concluding Remarks| <span style="color:red">Concluding Remarks </span> ]] | [[Citations|<span style="color:red">Citations</span>]]</center>
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 [[Stochasticity in a Eukaryotic Background|<span style="color:red">Stochasticity in a Eukaryotic Background</span>]]
 <br><br><Br>
-| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant sources from neglible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
+| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degradation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant sources from negligible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
-<br>A Prokaryotic vs. a Eukaryotic background is an example of an important distinction to make when characterizing stochasticity. Differences in transcriptional and translational processes lead to differences in dominant manifestations of non-genetic identity. In prokaryotes Ozbudak et. al. characterized a modular model of expression noise in prokaryotic ''Bacilus Subtillis''. In his model stochastic variables representing transcriptional and translational processes represented the manifestation of stochasticity in the system. JJ Collins et. al. suggested that promoter kinetics, and not simply protein abundance, was a factor depending on if the chasis was prokaryotic or eukaryotic. The abundance of characterizations of noise in different backgrounds poses an obstacle for comparision and suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say however that that stochastic events cannot be predicted and taken into account. Check out the In Depth section of this page to find out about the underlying motifs in stochastic processes across different backgrounds.
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 <center><b>[[Term Paper Wiki | Previous Section]] | [[Modeling Stochasticity | Next Section]] </b></center>

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 [[Stochasticity in a Eukaryotic Background|<span style="color:red">Stochasticity in a Eukaryotic Background</span>]]
 <br><br><Br>
-| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant causes from neglible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
+| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant sources from neglible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
 <br>A Prokaryotic vs. a Eukaryotic background is an example of an important distinction to make when characterizing stochasticity. Differences in transcriptional and translational processes lead to differences in dominant manifestations of non-genetic identity. In prokaryotes Ozbudak et. al. characterized a modular model of expression noise in prokaryotic ''Bacilus Subtillis''. In his model stochastic variables representing transcriptional and translational processes represented the manifestation of stochasticity in the system. JJ Collins et. al. suggested that promoter kinetics, and not simply protein abundance, was a factor depending on if the chasis was prokaryotic or eukaryotic. The abundance of characterizations of noise in different backgrounds poses an obstacle for comparision and suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say however that that stochastic events cannot be predicted and taken into account. Check out the In Depth section of this page to find out about the underlying motifs in stochastic processes across different backgrounds.

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 [[Stochasticity in a Eukaryotic Background|<span style="color:red">Stochasticity in a Eukaryotic Background</span>]]
 <br><br><Br>
-| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochasiticy is characterized as that which is inherent in the processes of transcription and translation (e.g. Gillsepe Model); extrinsic stochasticity is characterized as that which arises from other sources (e.g. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number.) While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant causes from neglible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
+| Manifestations of stochasticity in cellular protein production are observable in processes both intrinsic and extrinsic to the gene in question. Intrinsic stochastic processes are characterized as those which are inherent in transcription and translation (e.g. Gillsepe Model); extrinsic stochastic processes are characterized as those which arises from sources other than the gene in question (e.g. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number). While stochasticity in protein production comes from a variety sources, many labs have sought to distinguish dominant causes from neglible ones. Different experimental parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies.
 <br>A Prokaryotic vs. a Eukaryotic background is an example of an important distinction to make when characterizing stochasticity. Differences in transcriptional and translational processes lead to differences in dominant manifestations of non-genetic identity. In prokaryotes Ozbudak et. al. characterized a modular model of expression noise in prokaryotic ''Bacilus Subtillis''. In his model stochastic variables representing transcriptional and translational processes represented the manifestation of stochasticity in the system. JJ Collins et. al. suggested that promoter kinetics, and not simply protein abundance, was a factor depending on if the chasis was prokaryotic or eukaryotic. The abundance of characterizations of noise in different backgrounds poses an obstacle for comparision and suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say however that that stochastic events cannot be predicted and taken into account. Check out the In Depth section of this page to find out about the underlying motifs in stochastic processes across different backgrounds.