Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
This work addresses the challenge of constructing and training mathematical models in systems biology, specifically for signaling networks, by providing a more reliable and scalable method, though it is incremental as it builds on existing logic model training approaches.
The authors tackled the problem of training Boolean logic models of protein signaling networks to high-throughput phospho-proteomics data by using Answer Set Programming (ASP), a declarative approach, which improved efficiency, scalability, and guaranteed global optimality compared to previous heuristic methods.
A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data.