Modular control of Boolean network models

by David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, and Reinhard Laubenbacheri

Control problems in biological systems involve identifying effective interventions to drive the system toward a desired outcome. These strategies are typically guided by mathematical models, where the objective is to find suitable control inputs such as gene knockouts that alter system behavior in a predictable way. In this paper, we introduce a modular approach for controlling Boolean networks, which represent biological regulatory systems using binary logic. Our method decomposes a complex network into smaller, more manageable modules, allowing for the analysis and control of each subnetwork independently. By leveraging the modular structure and the canalizing properties of regulatory functions we develop a scalable framework for control.

Graphical abstract summarizing the study.