  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.
