Learning biochemical models from temporal logic properties

One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rule-based language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, differential and stochastic; and one temporal logic language used for formalizing the biological properties of the system. In this tutorial, we show how the temporal logic language can be turned into a specification language. We report on two model revision algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. With an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler.