Multi-modality in gene regulatory networks with slow promoter kinetics.

Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in determinis...

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Main Authors: M Ali Al-Radhawi, Domitilla Del Vecchio, Eduardo D Sontag
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-02-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC6396950?pdf=render
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spelling doaj-9b07c551fcd14347ad709837301096a72020-11-25T01:52:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-02-01152e100678410.1371/journal.pcbi.1006784Multi-modality in gene regulatory networks with slow promoter kinetics.M Ali Al-RadhawiDomitilla Del VecchioEduardo D SontagPhenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.http://europepmc.org/articles/PMC6396950?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author M Ali Al-Radhawi
Domitilla Del Vecchio
Eduardo D Sontag
spellingShingle M Ali Al-Radhawi
Domitilla Del Vecchio
Eduardo D Sontag
Multi-modality in gene regulatory networks with slow promoter kinetics.
PLoS Computational Biology
author_facet M Ali Al-Radhawi
Domitilla Del Vecchio
Eduardo D Sontag
author_sort M Ali Al-Radhawi
title Multi-modality in gene regulatory networks with slow promoter kinetics.
title_short Multi-modality in gene regulatory networks with slow promoter kinetics.
title_full Multi-modality in gene regulatory networks with slow promoter kinetics.
title_fullStr Multi-modality in gene regulatory networks with slow promoter kinetics.
title_full_unstemmed Multi-modality in gene regulatory networks with slow promoter kinetics.
title_sort multi-modality in gene regulatory networks with slow promoter kinetics.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2019-02-01
description Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.
url http://europepmc.org/articles/PMC6396950?pdf=render
work_keys_str_mv AT malialradhawi multimodalityingeneregulatorynetworkswithslowpromoterkinetics
AT domitilladelvecchio multimodalityingeneregulatorynetworkswithslowpromoterkinetics
AT eduardodsontag multimodalityingeneregulatorynetworkswithslowpromoterkinetics
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