Origins and Characterization of Stochasticity

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In Depth Overview of the Origins and Characterization of Stochasticity

Finite Number Effect

Translational Bursting

Promoter Kinetics

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 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.

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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