Phenotypic switching in gene regulatory networks

• Published in: Proceedings of the National Academy of Sciences - PNAS 2014-05, Vol.111 (19), p.6994-6999
• Main Authors: Thomas, Philipp, Popović, Nikola, Grima, Ramon
• Format: Article
• Language: English
• Subjects: Allosteric regulation; Animals; Approximation; Biological Sciences; Computer Simulation; Epigenomics; Feedback, Physiological; Gene expression; Gene Expression Regulation; Gene Regulatory Networks; Genes; Genotype & phenotype; Humans; Inductive reasoning; Messenger RNA; Models, Genetic; Molecules; Noise; Normal distribution; Oscillators; Phenotype; Phenotypes; Promoter Regions, Genetic; Proteins; Simulation; Stochastic Processes; Switching; Transcriptional regulatory elements
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1400049111

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.