  Stochastic simulation has been a powerful tool for studying the dynamics of
gene regulatory networks, particularly in terms of understanding how
cell-phenotype stability and fate-transitions are impacted by noisy gene
expression. However, gene networks often have dynamics characterized by
multiple attractors. Stochastic simulation is often inefficient for such
systems, because most of the simulation time is spent waiting for rare,
barrier-crossing events to occur. We present a rare-event simulation-based
method for computing epigenetic landscapes and phenotype-transitions in
metastable gene networks. Our computational pipeline was inspired by studies of
metastability and barrier-crossing in protein folding, and provides an
automated means of computing and visualizing essential stationary and dynamic
information that is generally inaccessible to conventional simulation. Applied
to a network model of pluripotency in Embryonic Stem Cells, our simulations
revealed rare phenotypes and approximately Markovian transitions among
phenotype-states, occurring with a broad range of timescales. The relative
probabilities of phenotypes and the transition paths linking pluripotency and
differentiation are sensitive to global kinetic parameters governing
transcription factor-DNA binding kinetics. Our approach significantly expands
the capability of stochastic simulation to investigate gene regulatory network
dynamics, which may help guide rational cell reprogramming strategies. Our
approach is also generalizable to other types of molecular networks and
stochastic dynamics frameworks.
