Investigating co-infection dynamics through evolution of Bio-PEPA model parameters: a combined process algebra and evolutionary computing approach

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

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.