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A Bayesian Approach to Model Checking Biological Systems

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Computational Methods in Systems Biology (CMSB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5688))

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Abstract

Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.

This research was sponsored by the GSRC (University of California) under contract no. SA423679952, National Science Foundation under contracts no. CCF0429120, no. CNS0411152, and no. CCF0541245, Semiconductor Research Corporation under contract no. 2005TJ1366, Air Force (University of Vanderbilt) under contract no. 18727S3, International Collaboration for Advanced Security Technology of the National Science Council, Taiwan, under contract no. 1010717, the U.S. Department of Energy Career Award (DE-FG02-05ER25696), and a Pittsburgh Life-Sciences Greenhouse Young Pioneer Award.

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Author information

Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, USA

    Sumit K. Jha, Edmund M. Clarke, Christopher J. Langmead, André Platzer & Paolo Zuliani

  2. Lane Center for Computational Biology, Carnegie Mellon University, USA

    Christopher J. Langmead

  3. Institut d’Informatique INRIA, Rennes, France

    Axel Legay

Authors
  1. Sumit K. Jha
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  2. Edmund M. Clarke
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  3. Christopher J. Langmead
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  4. Axel Legay
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  5. André Platzer
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  6. Paolo Zuliani
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Editors and Affiliations

  1. Dipartimento di Informatica, Università di Pisa, Largo Bruno Pontecorvo, 3, 56127, Pisa, Italy

    Pierpaolo Degano

  2. Dipartimento di Scienze dell’Informazione, Università degli Studi di Bologna, Mura Anteo Zamboni 7, 40127, Bologna, Italy

    Roberto Gorrieri

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Jha, S.K., Clarke, E.M., Langmead, C.J., Legay, A., Platzer, A., Zuliani, P. (2009). A Bayesian Approach to Model Checking Biological Systems. In: Degano, P., Gorrieri, R. (eds) Computational Methods in Systems Biology. CMSB 2009. Lecture Notes in Computer Science(), vol 5688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03845-7_15

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  • DOI: https://doi.org/10.1007/978-3-642-03845-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03844-0

  • Online ISBN: 978-3-642-03845-7

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