ABC-Fun: A Probabilistic Programming Language for Biology

Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti

Abstract: Formal methods have long been employed to capture the dynamics of biological systems in terms of Continuous Time Markov Chains. The formal approach enables the use of elegant analysis tools such as model checking, but usually relies on a complete specification of the model of interest and cannot easily accommodate uncertain data. In contrast, data-driven modelling, based on machine learning techniques, can fit models to available data but their reliance on low level mathematical descriptions of systems makes it difficult to readily transfer methods from one problem to the next. Probabilistic programming languages potentially offer a framework in which the strengths of these two approaches can be combined, yet their expressivity is limited at the moment.