Evolved Stochasticity?

2007-12-06T17:27:00Z

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Manipulation of Stochasticity

2007-12-06T17:26:52Z

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2007-12-06T17:24:28Z

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Correlative Data

2007-12-06T17:23:24Z

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MB Eisen et. al. used a correlative approach to study the evolution of gene noise. They used the assumption that lower translational efficiency results in lower stochasticity in the production of the gene. Next, they estimated protein production rates for all yeast genes in the yeast genome. They then experimentally measured mRNA levels and compared the estimated protein production level with the expressed mRNA level. Using a bin array they found that genes deemed essential and genes participating in the formation of large complexes were low in translational efficiency (they used a spearman partial test to determine whether their data was statistically significant). They hypothesize that the minimization of noise in a cell is expensive. Expense comes from need to continually synthesize and degrade mRNA transcripts and is therefore energetically unfavorable. Tight control over stochasticity is only selected for when the cost of wasting an entire protein complex or cell death outweighs the cost of minimizing stochastic translation.

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Evolved Stochasticity?

2007-12-06T17:22:15Z

Miwaters:

2007-12-06T17:16:20Z

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<center>[[Term Paper Wiki| Home]] | [[Origins and Characterization of Stochasticity| Origins and Characterization of Stochasticity]] | [[Modeling Stochasticity| Modeling Stochasticity]] | [[Manipulation of Stochasticity| Manipulation of Stochasticity]] | [[Evolved Stochasticity? | Evolved Stochasticity?]] | [[Concluding Remarks| Concluding Remarks ]] | [[Citations|Citations]]</center>
 
{| border="1" cellpadding="5" cellspacing="0" align="center" width="90%"
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! style="color: black; background-color: red;" width="20%"| In Depth
! colspan="3" style="color: black; background-color: red;" width="60%"| Overview of the Origins and Characterization of Stochasticity
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[[Finite Number Effect|Finite Number Effect]]
 
[[Translational Bursting|Translational Bursting]]
 
[[Promoter Kinetics|Promoter Kinetics]]
 
[[Stochasticity in a Eukaryotic Background|Stochasticity in a Eukaryotic Background]]
 
| 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.
 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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