AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.

ABSTRACT: Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navi

SUBMITTER: Etcheverry M 

PROVIDER: S-EPMC11729405 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature