Control-guided refinement of partially specified Boolean networks: applications to RTK signaling

System control can be used to provide new insights into the dynamics of biological systems. A key application is the identification of therapeutic targets in silico, which requires an executable model of the system's dynamics. However, such models are typically underspecified due to incomplete mechanistic knowledge.

We introduce a novel computational framework that employs control-guided model refinement, predicting informative perturbation experiments to reduce knowledge gaps. The approach is based on partially specified Boolean networks (PSBNs), which enable direct integration of uncertain or incomplete information into executable models. We further extend the framework to handle oscillatory phenotypes as explicit control targets. The applicability of the method is demonstrated on receptor-tyrosine kinase (RTK) signaling, with a focus on fibroblast growth factor signaling in the context of skeletal dysplasias and cancer. We obtain several new insights into modelling of the FGFR3-MAPK pathway.

Code and datasets are available at https://doi.org/10.5281/zenodo.16886813.