  Noise caused by fluctuations at the molecular level is a fundamental part of
intracellular processes. While the response of biological systems to noise has
been studied extensively, there has been limited understanding of how to
exploit it to induce a desired cell state. Here we present a scalable,
quantitative method based on the Freidlin-Wentzell action to predict and
control noise-induced switching between different states in genetic networks
that, conveniently, can also control transitions between stable states in the
absence of noise. We apply this methodology to models of cell differentiation
and show how predicted manipulations of tunable factors can induce lineage
changes, and further utilize it to identify new candidate strategies for cancer
therapy in a cell death pathway model. This framework offers a systems approach
to identifying the key factors for rationally manipulating biophysical
dynamics, and should also find use in controlling other classes of noisy
complex networks.
