Stochastic gene expression in bacterial pathogens: A mechanism for persistence?

Abstract

Recent experiments have shown the relevance of stochastic fluctuations to numerous biological phenomena. Intrinsic and extrinsic sources of noise existing at the subcellular level are capable of influencing the population dynamics and are believed to be responsible for the appearance of different phenotypes in clonal bacterial populations. Single cell level phenotypic diversity is a likely key factor in the emergence of persistence in Mycobacterium tuberculosis. Stochastic phenomena in molecular interaction networks have been first postulated in theoretical studies and later confirmed by experimental observations of individual cells and molecules. Here, we shall review the main modeling tools that can be used in this context, namely stochastic differential equations (Langevin equations) and Master Equations and their simulational counterparts, such as the Gillespie algorithm. We will distinguish between intrinsic and extrinsic noise in subcellular networks, highlighting in particular the unexpected and sometimes counterintuitive behaviors induced by extrinsic noise. We will discuss the dependence of prokaryotic gene expression noise on transcription and translation rates, as emerged from theoretical and experimental studies of stochasticity in biochemical processes. These findings have direct consequences for understanding more complex gene regulatory networks, such as catabolic repression and two-component systems. Finally we will discuss the insights into the emergence of persistence of M. tuberculosis resulting from our understanding of stochastic gene expression, and delineate directions of future research.