Exploratory adaptation in gene regulatory networks

Biological systems exhibit well-defined and repeatable responses, but can also explore, improvise and generate new functionality in the face of unforeseen conditions. At the level of populations, exploration can be implemented by variation and selection. At the level of the single cell or organism, exploratory behavior has been observed experimentally, but a theory or organizing principle is not well developed.
I will review experimental evidence for exploratory behavior in gene regulation, that stabilizes adaptive phenotypes in single cells with high probability and relatively short timescales. I will then present a model of gene regulatory networks which demonstrates the capacity for such adaptation. The model addresses the problem of convergence of exploration in high-dimensional spaces. It shows that such convergence is non-universal and depends on network properties: it requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes it a biologically plausible mechanism for primitive cellular learning.