Abstract
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.
Highlights
Cellular systems are not entirely deterministic, but are instead impacted by small, random fluctuations in the number and activity of molecules of intracellular species [1,2]
In this paper we propose a broadly applicable method, here termed optimal least action control (OLAC), that can predict and control the dynamical behavior and response to noise in a wide class of biophysical networks
These lineages depend on two dimensionless parameters, l1 and l2, that determine the levels of epidermal growth factor (EGF) and Notch signaling, respectively
Summary
Cellular systems are not entirely deterministic, but are instead impacted by small, random fluctuations in the number and activity of molecules of intracellular species [1,2]. The landscape representation has been given a quantitative foundation as the quasipotential of the deterministic component of the system dynamics [9] and has been explored in experiments, e.g., to show how two parameters in the yeast galactose signaling network, the concentrations of galactose and intracellular Gal80p, can alter the rates of stochastic switching in this bistable circuit [10] Despite these advances, researchers’ ability to control this landscape in order to induce prespecified biological outcomes has been generally limited to at most two parameters [11,12], and no general method currently exists to systematically tune transitions between stable states and/or eliminate undesired states altogether. The method proposed here is implementable and the computational effort scales linearly in the number of control parameters and the dimension of the state space, allowing our approach to be applied to large networks and high-dimensional systems in general
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