A tool for automated inference in rule-based biological models

Abstract

Rule-based biological models help researchers investigate systems such as cellular signalling pathways. Although these models are generally programmed by hand, some research efforts aim to program them automatically using biological facts extracted from papers via natural language processing. However, NLP facts cannot always be directly converted into mechanistic reaction rules for a rule-based model. Thus, there is a need for tools that can convert biological facts into mechanistic rules in a logically sound way. We construct such a tool specifically for Kappa, a model programming language, by implementing Iota, a logic language for Kappa models. Our tool can translate biological facts into Iota predicates, check predicates for satisfiability, and find models that satisfy predicates. We test our system against realistic use cases, and show that it can construct rule-based mechanistic models that are sound with respect to the semantics of the biological facts from which they were constructed.