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Automated Refinement of Executable Biological Models

Motivation: Executable models of biological phenomena offer a powerful way to understand and analyze the properties of complex systems. By exploiting model checking techniques developed in the field of formal verification, we avoid the need for computationally intensive, exhaustive simulation approaches. Proofs of stability specifically demonstrate that a model accurately represents a robust equilibrium or homeostasis. Building models which are stable is however nontrivial, and identifying the missing interactions that are required for stability can generate valuable experimental predictions. Results: We present an approach for automating the refinement of qualitative network models (QNs) by proposing novel protein-protein interactions. We extend our previous approaches for proving stability in QNs by calculating the map of dependencies between different components, and use this to guide the automated exploration of new potential interactions. These new interactions are scored based on their ability to increase the number of stable components in the QN model and based on this score and knowledge of the specific system, the user can select which specific interactions to add. We demonstrate the validity of our automated refinement method by applying it to the identification of known interactions in three well characterized signaling systems; aggregation networks in Dictyostelium discoideum, Wnt/Notch crosstalk in human skin, and LET-23/LIN-12 in Caenorhabditis elegans vulval development. Finally, we apply the approach to propose new interactions in hair follicle development. Availability and implementation: The BioModelAnalyzer is freely available for non-commercial use at http://biomodelanalyzer.research.microsoft.com/ Contact: benhall@microsoft.com