Stochastic Gene Expression: Modeling, Analysis, and Identification∗

Gene networks arise due to the interaction of genes through their protein products. Modeling such networks is key to understanding life at the most basic level. One of the emerging challenges to the analysis of genetic networks is that the cellular environment in which these genetic circuits function is abuzz with noise. The main source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a source of phenotypic variability among clonal cellular populations. In some instances fluctuations are suppressed downstream through intricate dynamical networks that act as noise filters. Yet in other important instances, noise induced fluctuations are exploited to the cell’s advantage. The richness of stochastic phenomena in biology depends directly upon the interactions of dynamics and noise and upon the mechanisms through which these interactions occur. In this article, we explore the origins and impact of cellular noise, drawing examples from endogenous and synthetic biological networks. We motivate the need for stochastic models and outline the key tools for the modeling and analysis of stochasticity inside living cells. We show that tools from system theory can be effectively utilized for modeling, analysis, and identification of gene networks. ∗This article is an expanded version of a conference paper that appeared in the proceedings of IFAC 2009 SYSID