Stochasticity and cell population heterogeneity in an artificial lac operon genetic network
Doctor of Philosophy
The purpose of this work is two-fold: (1) to develop a novel mathematical and computational framework that incorporates the major sources of cell population heterogeneity and (2) to use this framework to demonstrate the effect of stochasticity on cell population heterogeneity in an artificial lac operon genetic network. During the past decades, several approaches have been used to model heterogeneity in bacterial cell populations, each approach focusing on different source(s) of heterogeneity. However, a holistic approach that integrates all the major sources into a generic framework is still lacking. In this work we present a mathematical and computational framework that describes single cells or cell populations and takes into account stochasticity in reaction, division and DNA duplication, all of which constitute sources of cell population heterogeneity. We subsequently use this framework to demonstrate how stochasticity generates complex behavior and phenotypic heterogeneity in the case of an artificial lac operon genetic network, characteristic of positive feedback regulation. Our results show that stochasticity can enhance phenotypic heterogeneity, create or destroy bistability, and result in noise-induced transitions between attracting vicinities. We also found that it is possible to predict population averages with carefully constructed single cell models.