Control of stochastic and induced switching in biophysical networks

TL;DR

This paper introduces a scalable, quantitative method based on Freidlin-Wentzell action to predict and control noise-induced state switching in genetic networks, with applications to cell differentiation and cancer therapy.

Contribution

It presents a novel, systems-level approach for manipulating biophysical dynamics in noisy networks, enabling control of cell states and transitions.

Findings

Predicts noise-induced switching in genetic networks

Demonstrates control of cell differentiation pathways

Identifies strategies for cancer therapy targeting cell death pathways

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.