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Probabilistic Model Checking of the PDGF Signaling Pathway

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Transactions on Computational Systems Biology XIV

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 7625))

Abstract

In this paper, we apply the probabilistic symbolic model checker PRISM to the analysis of a biological system – the Platelet-Derived Growth Factor (PDGF) signaling pathway, demonstrating in detail how this pathway can be analyzed in PRISM. Moreover, we compare the results from verification and ODE simulation on the PDGF pathway and demonstrate by examples the influence of model structure, parameter values and pathway length on the two analysis methods.

An extended abstract appears in the proceedings of CompMod 2011 [1]. The first two authors made equal contributions to this work.

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Author information

Authors and Affiliations

  1. Computer Science and Communications, University of Luxembourg, Luxembourg

    Qixia Yuan, Jun Pang & Sjouke Mauw

  2. School of Computer Science and Technology, Shandong University, China

    Qixia Yuan

  3. Life Sciences Research Unit, University of Luxembourg, Luxembourg

    Panuwat Trairatphisan, Monique Wiesinger & Thomas Sauter

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  1. Qixia Yuan
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  2. Panuwat Trairatphisan
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  3. Jun Pang
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  4. Sjouke Mauw
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  5. Monique Wiesinger
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  6. Thomas Sauter
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Editors and Affiliations

  1. Centre for Computational and Systems Biology, The Microsoft Research - University of Trento, Piazza Manci, 17, 38050, Povo, TN, Italy

    Corrado Priami

  2. Department of Information Technologies, Akademi University, Joukohaisenkatu 3-5, 20520, Turku, Finland

    Ion Petre

  3. Technische Universiteit Eindhoven, Den Dolech 2, Eindhoven, The Netherlands

    Erik de Vink

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Yuan, Q., Trairatphisan, P., Pang, J., Mauw, S., Wiesinger, M., Sauter, T. (2012). Probabilistic Model Checking of the PDGF Signaling Pathway. In: Priami, C., Petre, I., de Vink, E. (eds) Transactions on Computational Systems Biology XIV. Lecture Notes in Computer Science(), vol 7625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35524-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-35524-0_7

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