Phenotypic switching in gene regulatory networks.
Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.
School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom;School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom; andSynthSys, Edinburgh EH9 3JD, United Kingdom.
Philipp
P
Thomas
School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom;
Nikola
N
Popović
School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom; andSynthSys, Edinburgh EH9 3JD, United Kingdom ramon.grima@ed.ac.uk.
Ramon
R
Grima
D000818:Animals; D003198:Computer Simulation; D057890:Epigenomics; D025461:Feedback, Physiological; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D011401:Promoter Regions, Genetic; D013269:Stochastic Processes
chemical master equation; gene expression noise