Exploratory Adaptation in Large Random Networks

TL;DR

This paper demonstrates that high-dimensional gene regulatory networks can adapt through exploratory dynamics initiated by failure, relying on network hubs and auto-regulation, suggesting a plausible biological mechanism for flexible adaptation.

Contribution

It introduces a model showing how exploratory adaptation occurs in high-dimensional gene networks, highlighting the roles of hubs and auto-regulation without fine-tuning.

Findings

Successful adaptation requires network hubs.

Auto-regulation enhances adaptation.

Exploratory dynamics are feasible in high-dimensional networks.

Abstract

The capacity of cells and organisms to respond to challenging conditions in a repeatable manner is limited by a finite repertoire of pre-evolved adaptive responses. Beyond this capacity, cells can use exploratory dynamics to cope with a much broader array of conditions. However, the process of adaptation by exploratory dynamics within the lifetime of a cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional network model of gene regulation. Exploration is initiated by failure to comply with a constraint and is implemented by random sampling of network configurations. It ceases if and when the network reaches a stable state satisfying the constraint. We find that successful convergence (adaptation) in high dimensions 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 plausible for biological implementation.