SIGNAL: Stochastic process algebra for biochemical signalling pathway analysis

Lead Research Organisation: University of Glasgow
Department Name: School of Computing Science

Abstract

The science of computational Systems Biology uses computer modellingof living organisms to help us understand those process at work insidewhich we cannot directly see or measure. Based on experimentallydetermined data, a systems biologist invents a model of how they thinkthat things work. The model is usually analysed by one of two kindsof computer simulation: stochastic or deterministic. In a stochasticsimulation the mathematical theory of probability is used to express adegree of uncertainty about how fast reactions happen or thequantities of reactants which are in use. In a deterministicsimulation the theory of ordinary differential equations is used togive an efficient continuous approximation of very large numbers ofmolecular elements. Computational modelling is helpful here becauselaboratory-based experimentation is an extremely expensive,time-consuming and labour-intensive process. An important subject forcomputational modelling is signal transduction.Signal transduction pathways are biochemical pathways which allowcells to sense a stimulus and communicate a signal to the nucleus,which then makes a suitable response. They are complicated signallingprocesses with built-in feedback mechanisms. Signalling pathways areembedded in larger networks and are involved in important processessuch as proliferation, cell growth, movement, cell communication, andprogrammed cell death (apoptosis). Malfunction results in a largenumber of diseases including cancer, diabetes and many others. Despiteenormous experimental advances in recent years there is still anabsence of good, predictive pathway models which can guideexperimentation and drug development. To date, models either encodestatic aspects such as which proteins have the potential to interact,or provide simulations of system dynamics using ordinary differentialequations.We will develop a novel approach to analytic pathway modelling basedon our experience of modelling concurrent computing systems. The keyidea is that pathways have stochastic, computational content. We willmodel pathways using stochastic process algebras which denotecontinuous time Markov chains thus affording new quantitative analysisand new ways to structure pathways and reason about incompletebehaviour.

Publications

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Benford S (2016) On Lions, Impala, and Bigraphs Modelling Interactions in Physical/Virtual Spaces in ACM Transactions on Computer-Human Interaction

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Andrei O (2010) A Model and Analysis of the AKAP Scaffold in Electronic Notes in Theoretical Computer Science

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Ciocchetta F (2009) Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA in Electronic Notes in Theoretical Computer Science

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Degasperi A (2010) Relating PDEs in Cylindrical Coordinates and CTMCs with Levels of Concentration in Electronic Notes in Theoretical Computer Science

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Donaldson R (2012) Modular modelling of signalling pathways and their cross-talk in Theoretical Computer Science

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Degasperi A (2013) A process algebra framework for multi-scale modelling of biological systems in Theoretical Computer Science

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Boca, Paul P.; Bowen, Jonathan P.; Siddiqi, Jawed I. (2009) Formal Methods: State of the Art and New Directions

 
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Sector Communities and Social Services/Policy
Impact Types Economic