Investigating Autocatalytic Gene Expression Systems through Mechanistic Modeling

Abstract

A structured model of gene expression, which incorporates the stochastic behavior of cellular processes, was developed to examine the “all-or-none” phenomenon observed in autocatalytic systems (e.g. the lac operon). Autocatalytic expression systems typically have the genes encoding the inducer transport proteins controlled by internal inducer levels, so that transport of the inducer increases production of the transport protein. The model was able to predict the unique behaviors of autocatalytic expression systems that have been experimentally observed and provided valuable insight into the role of population heterogeneity in these systems. The simulations substantiate the importance of stochastic processes on induction of gene expression in autocatalytic systems. The simulation results show that the all-or-none phenomenon is governed largely by random cellular events, and that population-averaged variations in gene expression are due to changes in the frequency of full gene induction in individual cells rather than to uniform variations in gene expression across the entire population. In addition, the model shows how concentrations of inducer too low to induce expression in uninduced cells can maintain induction in pre-induced cultures. A comparison of induction behaviors from an autocatalytic system and a system having constitutive synthesis of the transport protein showed that transport protein levels must be decoupled from inducer control to achieve homogeneous expression of a gene of interest in all cells of a culture.