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ABC–Fun: A Probabilistic Programming Language for Biology

  • Anastasis Georgoulas
  • Jane Hillston
  • Guido Sanguinetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8130)

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.

We propose a high-level framework for specifying and performing inference on descriptions of models using a probabilistic programming language. We extend the expressivity of an existing probabilistic programming language, Infer.NET Fun, in order to enable inference and simulation of CTMCs. We demonstrate our method on simple test cases, including a more complex model of gene expression. Our results suggest that this is a promising approach with room for future development on the interface between formal methods and machine learning.

Keywords

Posterior Distribution Markov Chain Monte Carlo Inference Engine Approximate Bayesian Computation Continuous Time Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anastasis Georgoulas
    • 1
  • Jane Hillston
    • 1
    • 2
  • Guido Sanguinetti
    • 1
    • 2
  1. 1.School of InformaticsUniversity of EdinburghUK
  2. 2.SynthSys — Synthetic and Systems BiologyUniversity of EdinburghUK

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