Literature DB >> 18662776

The stochastic nature of biochemical networks.

Vahid Shahrezaei1, Peter S Swain.   

Abstract

Cell behaviour and the cellular environment are stochastic. Phenotypes vary across isogenic populations and in individual cells over time. Here we will argue that to understand the abilities of cells we need to understand their stochastic nature. New experimental techniques allow gene expression to be followed in single cells over time and reveal stochastic bursts of both mRNA and protein synthesis in many different types of organisms. Stochasticity has been shown to be exploited by bacteria and viruses to decide between different behaviours. In fluctuating environments, cells that respond stochastically can out-compete those that sense environmental changes, and stochasticity may even have contributed to chromosomal gene order. We will focus on advances in modelling stochasticity, in understanding its effects on evolution and cellular design, and on means by which it may be exploited in biotechnology and medicine.

Mesh:

Year:  2008        PMID: 18662776     DOI: 10.1016/j.copbio.2008.06.011

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  63 in total

1.  Identifying sources of variation and the flow of information in biochemical networks.

Authors:  Clive G Bowsher; Peter S Swain
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-23       Impact factor: 11.205

2.  Reproducible science.

Authors:  Arturo Casadevall; Ferric C Fang
Journal:  Infect Immun       Date:  2010-09-27       Impact factor: 3.441

3.  Information processing in the adaptation of Saccharomyces cerevisiae to osmotic stress: an analysis of the phosphorelay system.

Authors:  Friedemann Uschner; Edda Klipp
Journal:  Syst Synth Biol       Date:  2014-04-19

4.  Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network.

Authors:  Taylor Firman; Anar Amgalan; Kingshuk Ghosh
Journal:  J Phys Chem B       Date:  2019-01-09       Impact factor: 2.991

5.  Analytical distributions for stochastic gene expression.

Authors:  Vahid Shahrezaei; Peter S Swain
Journal:  Proc Natl Acad Sci U S A       Date:  2008-11-06       Impact factor: 11.205

6.  Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories.

Authors:  Rory M Donovan; Andrew J Sedgewick; James R Faeder; Daniel M Zuckerman
Journal:  J Chem Phys       Date:  2013-09-21       Impact factor: 3.488

7.  Cross-talk between signaling pathways can generate robust oscillations in calcium and cAMP.

Authors:  Fernando Siso-Nadal; Jeffrey J Fox; Stéphane A Laporte; Terence E Hébert; Peter S Swain
Journal:  PLoS One       Date:  2009-10-21       Impact factor: 3.240

Review 8.  From the stochasticity of molecular processes to the variability of synaptic transmission.

Authors:  Claire Ribrault; Ken Sekimoto; Antoine Triller
Journal:  Nat Rev Neurosci       Date:  2011-06-20       Impact factor: 34.870

9.  Exact and approximate distributions of protein and mRNA levels in the low-copy regime of gene expression.

Authors:  Pavol Bokes; John R King; Andrew T A Wood; Matthew Loose
Journal:  J Math Biol       Date:  2011-06-08       Impact factor: 2.259

10.  Transgenerational stress memory is not a general response in Arabidopsis.

Authors:  Ales Pecinka; Marisa Rosa; Adam Schikora; Marc Berlinger; Heribert Hirt; Christian Luschnig; Ortrun Mittelsten Scheid
Journal:  PLoS One       Date:  2009-04-21       Impact factor: 3.240

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