Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching. In: 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, Stirling, UK, 01-03 Sep 2016, pp. 145-159. ISBN 9783319678337 (doi: 10.1007/978-3-319-67834-4_12)

129180.pdf - Accepted Version



Parameter inference in mechanistic models of biopathways based on systems of coupled differential equations is a topical yet computationally challenging problem, due to the fact that each parameter adaptation involves a numerical integration of the differential equations. Techniques based on gradient matching, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differential equations, offer a computationally appealing shortcut to the inference problem. However, gradient matching critically hinges on the smoothing scheme for function interpolation, with spurious wiggles in the interpolant having a dramatic effect on the subsequent inference. The present article demonstrates that a time warping approach aiming to homogenize intrinsic functional length scales can lead to a signifi- cant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from a dynamical system with periodic limit cycle and a biopathway.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Rogers, Dr Simon and Filippone, Dr Maurizio and Niu, Dr Mu
Authors: Niu, M., Rogers, S., Filippone, M., and Husmeier, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics
Published Online:17 October 2017
Copyright Holders:Copyright © 2017 Springer International Publishing AG
First Published:First published in Lecture Notes in Computer Science 10477: 145-159
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS