Ontology type: schema:Chapter Open Access: True
2017-10-17
AUTHORSMu Niu , Simon Rogers , Maurizio Filippone , Dirk Husmeier
ABSTRACTParameter 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... »
PAGES145-159
Computational Intelligence Methods for Bioinformatics and Biostatistics
ISBN
978-3-319-67833-7
978-3-319-67834-4
http://scigraph.springernature.com/pub.10.1007/978-3-319-67834-4_12
DOIhttp://dx.doi.org/10.1007/978-3-319-67834-4_12
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