Analytic methods for modeling stochastic regulatory networks

TL;DR

This paper reviews mathematical approaches to modeling stochastic biochemical and genetic networks, emphasizing probability distribution methods over traditional stochastic simulations, and highlights cases where analytical solutions are feasible.

Contribution

It introduces and discusses analytic methods for modeling stochastic regulatory networks, providing derivations and examples where analytical progress is possible.

Findings

Analytic solutions can be derived for certain stochastic network models.

Probability distribution approaches offer insights beyond simulation.

The review highlights cases with tractable analytical progress.

Abstract

The past decade has seen a revived interest in the unavoidable or intrinsic noise in biochemical and genetic networks arising from the finite copy number of the participating species. That is, rather than modeling regulatory networks in terms of the deterministic dynamics of concentrations, we model the dynamics of the probability of a given copy number of the reactants in single cells. Most of the modeling activity of the last decade has centered on stochastic simulation of individual realizations, i.e., Monte-Carlo methods for generating stochastic time series. Here we review the mathematical description in terms of probability distributions, introducing the relevant derivations and illustrating several cases for which analytic progress can be made either instead of or before turning to numerical computation.