Computational Inference in Systems Biology

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9044)


Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem. The computational costs associated with repeatedly solving the ODEs are often high. Aimed at reducing this cost, new concepts using gradient matching have been proposed. This paper combines current adaptive gradient matching approaches, using Gaussian processes, with a parallel tempering scheme, and conducts a comparative evaluation with current methods used for parameter inference in ODEs.


Parameter Inference Ordinary Differential Equations Gradient Matching Parallel Tempering Gaussian Processes 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of GlasgowScotland

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