Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-10-17

AUTHORS

Mu Niu , Simon Rogers , Maurizio Filippone , Dirk Husmeier

ABSTRACT

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. Gradient matching critically hinges on the smoothing scheme for function interpolation, with spurious oscillations in the interpolant having a dramatic effect on the subsequent inference. The present article demonstrates that a time warping approach that aims to homogenize intrinsic functional length scales can lead to a significant 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 model. More... »

PAGES

145-159

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67834-4_12

DOI

http://dx.doi.org/10.1007/978-3-319-67834-4_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092235700


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