Difference between revisions of "Origins and Characterization of Stochasticity"
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[[Stochasticity in a Eukaryotic Background|<span style="color:red">Stochasticity in a Eukaryotic Background</span>]] | [[Stochasticity in a Eukaryotic Background|<span style="color:red">Stochasticity in a Eukaryotic Background</span>]] | ||
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| − | |<center> 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 (ex. Gillsepe Model); extrinsic stochasticity is characterized as that which arises from other sources (ex. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number.) While in theory stochasticity of protein production comes from many sources, many labs have sought to distinguish dominant causes from neglible ones. Different parameters and independent variables aside, there are underlying motifs in the characterization of | + | |<center> 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 (ex. Gillsepe Model); extrinsic stochasticity is characterized as that which arises from other sources (ex. Presence of RNAP/ribosomes/mRNA degredation machinery, stage in cell cycle, or plasmid copy number.) While in theory stochasticity of protein production comes from many sources, many labs have sought to distinguish dominant causes from neglible ones. Different parameters and independent variables aside, there are underlying motifs in the characterization of stochasticity between studies. |
A Prokaryotic vs. a Eukaryotic background is 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 transcription and translation were two sources of stochasticity. JJ Collins et. al. suggested that promoter kinetics were also a factor depending on the system. The abundance of characterizations of noise and backgrounds suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say that that stochastic events cannot be predicted and taken into account. | A Prokaryotic vs. a Eukaryotic background is 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 transcription and translation were two sources of stochasticity. JJ Collins et. al. suggested that promoter kinetics were also a factor depending on the system. The abundance of characterizations of noise and backgrounds suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say that that stochastic events cannot be predicted and taken into account. | ||
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Revision as of 01:23, 13 November 2007
| In Depth | Overview of the Origins and Characterization of Stochasticity | ||
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Finite Number Effect
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A Prokaryotic vs. a Eukaryotic background is 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 transcription and translation were two sources of stochasticity. JJ Collins et. al. suggested that promoter kinetics were also a factor depending on the system. The abundance of characterizations of noise and backgrounds suggests that the significance of stochastic events truly depends on the nature of the system. This is not to say that that stochastic events cannot be predicted and taken into account. | ||
