Exact lower and upper bounds on stationary moments in stochastic biochemical systems

Khem Raj Ghusinga, Cesar A. Vargas-Garcia, Andrew Lamperski, Abhyudai Singh

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

In the stochastic description of biochemical reaction systems, the time evolution of statistical moments for species population counts is described by a linear dynamical system. However, except for some ideal cases (such as zero- and first-order reaction kinetics), the moment dynamics is underdetermined as lower-order moments depend upon higher-order moments. Here, we propose a novel method to find exact lower and upper bounds on stationary moments for a given arbitrary system of biochemical reactions. The method exploits the fact that statistical moments of any positive-valued random variable must satisfy some constraints that are compactly represented through the positive semidefiniteness of moment matrices. Our analysis shows that solving moment equations at steady state in conjunction with constraints on moment matrices provides exact lower and upper bounds on the moments. These results are illustrated by three different examples - the commonly used logistic growth model, stochastic gene expression with auto-regulation and an activator-repressor gene network motif. Interestingly, in all cases the accuracy of the bounds is shown to improve as moment equations are expanded to include higher-order moments. Our results provide avenues for development of approximation methods that provide explicit bounds on moments for nonlinear stochastic systems that are otherwise analytically intractable.

Original languageEnglish (US)
Article number04LT01
JournalPhysical Biology
Volume14
Issue number4
DOIs
StatePublished - Jun 29 2017

Bibliographical note

Funding Information:
AS acknowledges funding support from NSF DMS-1312926. The authors thank Mohammad Soltani for discussions on some of the ideas contained herein.

Keywords

  • biochemical systems
  • moment approximations
  • stationary moments

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