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ABC-Fun: A Probabilistic Programming Language for Biology

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationComputational Methods in Systems Biology
Subtitle of host publication11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013. Proceedings
EditorsAshutosh Gupta, Thomas A. Henzinger
PublisherSpringer-Verlag GmbH
Pages150-163
Number of pages14
ISBN (Electronic)978-3-642-40708-6
ISBN (Print)978-3-642-40707-9
DOIs
Publication statusPublished - 2013
EventThe 11th Conference on Computational Methods in Systems Biology (CMSB 2013) - IST Austria, Klosterneuburg, Austria
Duration: 23 Sep 201325 Sep 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
Volume8130
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 11th Conference on Computational Methods in Systems Biology (CMSB 2013)
CountryAustria
CityKlosterneuburg
Period23/09/1325/09/13

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.

Event

The 11th Conference on Computational Methods in Systems Biology (CMSB 2013)

23/09/1325/09/13

Klosterneuburg, Austria

Event: Conference

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