Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22206
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Marco, David
Scott, Erin
Cairns, David
Graham, Andrea
Allen, Judith E
Mahajan, Simmi M
Shankland, Carron
Contact Email: ces@cs.stir.ac.uk
Title: Investigating co-infection dynamics through evolution of Bio-PEPA model parameters: a combined process algebra and evolutionary computing approach
Editor(s): Gilbert, D
Heiner, M
Citation: Marco D, Scott E, Cairns D, Graham A, Allen JE, Mahajan SM & Shankland C (2012) Investigating co-infection dynamics through evolution of Bio-PEPA model parameters: a combined process algebra and evolutionary computing approach. In: Gilbert D & Heiner M (eds.) Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Lecture Notes in Computer Science, 7605. The 10th Conference on Computational Methods in Systems Biology, CMSB 2012, London, UK, 03.10.2012-05.10.2012. Berlin Heidelberg: Springer-Verlag, pp. 227-246. http://sites.brunel.ac.uk/cmsb2012; https://doi.org/10.1007/978-3-642-33636-2_14
Issue Date: 2012
Date Deposited: 3-Sep-2015
Series/Report no.: Lecture Notes in Computer Science, 7605
Conference Name: The 10th Conference on Computational Methods in Systems Biology, CMSB 2012
Conference Dates: 2012-10-03 - 2012-10-05
Conference Location: London, UK
Abstract: Process algebras are an effective method for defining models of complex interacting biological processes, but defining a model requires expertise from both modeller and domain expert. In addition, even with the right model, tuning parameters to allow model outputs to match experimental data can be difficult. This is the well-known parameter fitting problem. Evolutionary algorithms provide effective methods for finding solutions to optimisation problems with large search spaces and are well suited to investigating parameter fitting problems. We present the Evolving Process Algebra (EPA) framework which combines an evolutionary computation approach with process algebra modelling to produce parameter distribution data that provides insight into the parameter space of the biological system under investigation. The EPA framework is demonstrated through application to a novel example: T helper cell activation in the immune system in the presence of co-infection.
Status: VoR - Version of Record
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: http://sites.brunel.ac.uk/cmsb2012
Licence URL(s): http://www.rioxx.net/licenses/under-embargo-all-rights-reserved

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