Markov State Models of Gene Regulatory Networks

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Network...

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Bibliographic Details
Published inarXiv.org
Main Authors Chu, Brian K, Tse, Margaret J, Sato, Royce R, Read, Elizabeth L
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.10.2016
Subjects
Clustering
Gene expression
Identification methods
Markov chains
Molecular dynamics
Organic chemistry
Switching theory
Transportation networks
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Summary:Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. We present a method for analyzing global dynamics of gene networks using the Markov State Model (MSM) framework, which utilizes a separation-of-timescales based clustering method to obtain a coarse-grained state transition graph that approximates global gene network dynamics. The method identifies the number, phenotypes, and lifetimes of long-lived network states. Application of transition path theory to the constructed MSM decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the MSM provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that the MSM framework, adopted from the field of atomistic Molecular Dynamics, can be a useful tool for quantitative Systems Biology at the network scale.
ISSN:2331-8422