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.
Parameter inference in differential equation models of biopathways using time warped gradient matching
CIBB 2016, 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 1-3 September 2016, Stirling, UK / Also in LNCS, Vol.10477
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in CIBB 2016, 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 1-3 September 2016, Stirling, UK / Also in LNCS, Vol.10477 and is available at : http://doi.org/10.1007/978-3-319-67834-4_12
PERMALINK : https://www.eurecom.fr/publication/5355