Cell fate reprogramming by control of intracellular network dynamics

TL;DR

This paper introduces a new network control framework that predicts effective control targets for reprogramming cell fate, demonstrated on leukemia and T cell differentiation networks, with experimental support for some interventions.

Contribution

A novel logical dynamic control method integrating structural and functional data to identify effective cell fate reprogramming targets.

Findings

100% effectiveness in driving networks to target states

Predicted interventions supported by experimental evidence

Applicable to cancer treatment and cell differentiation

Abstract

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.