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Journal ArticleDOI

Stochastic switching in biology: From genotype to phenotype

Paul C. Bressloff
- 27 Feb 2017 - 
- Vol. 50, Iss: 13, pp 133001
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TLDR
A selfcontained survey of stochastic hybrid systems, large deviations and the Wentzel–Kramers–Brillouin method, adiabatic reductions, and queuing/renewal theory is provided.
Abstract
There has been a resurgence of interest in non-equilibrium stochastic processes in recent years, driven in part by the observation that the number of molecules (genes, mRNA, proteins) involved in gene expression are often of order 1–1000. This means that deterministic mass-action kinetics tends to break down, and one needs to take into account the discrete, stochastic nature of biochemical reactions. One of the major consequences of molecular noise is the occurrence of stochastic biological switching at both the genotypic and phenotypic levels. For example, individual gene regulatory networks can switch between graded and binary responses, exhibit translational/ transcriptional bursting, and support metastability (noise-induced switching between states that are stable in the deterministic limit). If random switching persists at the phenotypic level then this can confer certain advantages to cell populations growing in a changing environment, as exemplified by bacterial persistence in response to antibiotics. Gene expression at the single-cell level can also be regulated by changes in cell density at the population level, a process known as quorum sensing. In contrast to noise-driven phenotypic switching, the switching mechanism in quorum sensing is stimulus-driven and thus noise tends to have a detrimental effect. A common approach to modeling stochastic gene expression is to assume a large but finite system and to approximate the discrete processes by continuous processes using a systemsize expansion. However, there is a growing need to have some familiarity with the theory of stochastic processes that goes beyond the standard topics of chemical master equations, the system-size expansion, Langevin equations and the Fokker–Planck equation. Examples include stochastic hybrid systems (piecewise deterministic Markov processes), large deviations and the Wentzel–Kramers–Brillouin (WKB) method, adiabatic reductions, and queuing/renewal theory. The major aim of this review is to provide a selfcontained survey of these mathematical methods, mainly within the context of biological switching processes at both the genotypic and phenotypic levels. P C Bressloff Stochastic switching in biology: from genotype to phenotype Printed in the UK 133001 JPHAC5 © 2017 IOP Publishing Ltd 50 J. Phys. A: Math. Theor.

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Journal ArticleDOI

Stochastic Processes in Physics and Chemistry

D Sherrington
- 01 Apr 1983 - 
TL;DR: Van Kampen as mentioned in this paper provides an extensive graduate-level introduction which is clear, cautious, interesting and readable, and could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes.
Journal ArticleDOI

Linear mapping approximation of gene regulatory networks with stochastic dynamics

TL;DR: A linear-mapping approximation is presented that maps systems with protein–promoter interactions onto approximately equivalent systems with no binding reactions, giving approximate but accurate analytic or semi- analytic solutions for a wide range of model GRNs.
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Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.

TL;DR: This review presents an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models within the life sciences community.
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Emergent Lévy behavior in single-cell stochastic gene expression.

TL;DR: Two types of "macroscopic limits" are established: the Kurtz limit is consistent with the classical chemical kinetics, while the Lévy limit provides a theoretical foundation for an empirical equation proposed in N. Friedman et al., Phys.
Journal ArticleDOI

Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.

TL;DR: Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling, and characterizing stochastic effects in biochemical systems is essential to... as discussed by the authors.
References
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Neurons with graded response have collective computational properties like those of two-state neurons.

TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book

Neurons with graded response have collective computational properties like those of two-state neurons

TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI

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TL;DR: The synthesis of enzymes in bacteria follows a double genetic control, which appears to operate directly at the level of the synthesis by the gene of a shortlived intermediate, or messenger, which becomes associated with the ribosomes where protein synthesis takes place.
Journal ArticleDOI

Stochastic Gene Expression in a Single Cell

TL;DR: This work constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated and reveals how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
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