Stochastic Gene Expression: Modeling, Analysis, and Identification

Abstract

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 uctuations (over time) within individual cells, but it is also a source of phenotypic variability among clonal cellular populations. In some instances uctuations are suppressed downstream through an intricate dynamical network that acts as noise filters. Yet in other important instances, noise induced uctuations are exploited to the cells 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 paper, 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 effiectively utilized for modeling, analysis, and identification of gene networks.