Learning in gene regulatory networks: dimensionality reduction by master regulators

Under stressful and unforeseen challenge, cells can harness their internal complexity and plasticity to acquire novel adaptive phenotypes. Such exploratory adaptation is a primitive analog of learning; its dynamics and properties are not well understood. I will present a computational framework to describe such adaptation inspired by learning models of the brain. A random network model of gene regulation shows the feasibility of exploratory adaptation based on purely stochastic dynamics and stress sensing. Convergence in high-dimensional gene expression space is non-universal and depends on network properties: Specifically, convergence is promoted by heterogeneous connections with outgoing hubs – “master regulators”, a known feature of gene networks. We construct a coarse-grained model for understanding the role of hubs in the search process, that maps the problem onto suppression of chaotic network activity by an external drive. The phase transition in that problem sheds light on the role of master regulators in reducing search dimensionality.