Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

By Thomas B. Kepler, Timothy C. Elston. Biophysical Journal, 2001

926 citations recorded by the source corpus.

Subfields: Genetics, Modeling and Simulation, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eebac13e50b09aad2091d33a4c74d8c7dd44a4c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Modeling and Simulation

In Molecular Biology

Citations in the corpus

Stochastic switching in biology: from genotype to phenotype (2017, methodology): “Kepler and Elston [151] consider two approximations of the master equation, one based on a system size expansion of the Wj terms with respect to the mean number α γ = N / of proteins when the promoter is in state O0, and the other based on a quasi-steady-state (QSS) approximation.”
Analytical distributions for stochastic gene expression (2008, methodology): “Following others [7, 9, 10, 11], we will use this time-averaging to simplify the mathematical description of stochastic gene expression.”
Real-time kinetics of gene activity in individual bacteria. (2005, methodology): “Cell Bursts of Transcription To explain the observed heterogeneity, we return to previous models of random gene activation-inactivation (Kepler and Elston, 2001; Peccoud and Ycart, 1995; Raser and O’Shea, 2004; Sasai and Wolynes, 2003).”
Multistability in the lactose utilization network of Escherichia coli (2004, background): “: the discontinuous transition from low to high induction is analogous to a first-order phase transition such as evaporation in a liquid–gas system, with chemical noise instead of thermal noise driving stochastic transitions between these state”
Control, exploitation and tolerance of intracellular noise (2002, background): “Numerous other models have further validated and extended this hypothesis by analysing the mechanisms contributing to noise in gene expressio”
Stochastic gene expression and its consequences (2008, background): “…models of transcriptional bursting add another important source of stochasticity beyond random events in transcription and translation, which have now been analyzed theoretically in some detail (Friedman et al., 2006; Karmakar and Bose, 2004; Kepler and Elston, 2001; Pedraza and Paulsson, 2008).”
Convergence of stochastic gene networks to hybrid piecewise deterministic processes (2011, background): “Recent experimental advances in molecular biology show that many gene products do not follow deterministic dynamics and should be modeled as random variables (Kepler and Elston (2001); Kaufmann and van Oudenaarden (2007)).”
Engineered gene circuits (2002, background): “The importance of tightly controlled amounts of cellular protein has led other researchers to model how specific network properties might act to decrease or utilize fluctuation”
Stochastic switching as a survival strategy in fluctuating environments (2008, background): “Bistable gene networks provide promising experimental systems in which to implement discrete phenotypic states, and the stochastic nature of gene expression drives rare transitions between these state”
Regulation of noise in the expression of a single gene (2002, neutral): “This approach yields statistics equivalent to those generated by large-scale Monte Carlo simulations, but has the added advantage of providing insight into system behavio”