Speaker
Description
Control of biological networks is often achieved by targeting a small number of regulatory nodes, but identifying optimal control strategies remains challenging. Existing control methods frequently yield multiple alternative control sets that satisfy the same objective, making it difficult to select among them. Optimality is typically defined by minimality or by minimizing a cost function. This talk presents a framework for prioritizing control strategies using mutation scores and the modular structure of Boolean networks. Within a stochastic Boolean network setting, mutation scores are computed by simulating the effects of available control interventions. We apply this approach to a pancreatic cancer model, where candidate controls are identified through network modularity and ranked using mutation scores to select an optimal control set. We assess the feasibility of these controls and discuss their biological relevance. Finally, we outline key challenges and potential extensions of this approach to broader classes of biological networks.
