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Maier, C. ; Loos, C. ; Hasenauer, J.

Robust parameter estimation for dynamical systems from outlier-corrupted data.

Bioinformatics 33, 1-8 (2017)
Publ. Version/Full Text Postprint Research data DOI
Open Access Green
Motivation: Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers. Results: In this manuscript, we propose and evaluate methods for estimating the parameters of ordinary differential equation (ODE) models from outlier-corrupted data. As alternatives to the normal distribution as noise distribution, we consider the Laplace, the Huber, the Cauchy and the Student’s t distribution. We assess accuracy, robustness and computational efficiency of estimators using these different distribution assumptions. To this end, we consider artificial data of a conversion process, as well as published experimental data for Epo-induced JAK/STAT signaling. We study how well the methods can compensate and discover artificially introduced outliers. Our evaluation reveals that using alternative distributions improves the robustness of parameter estimates. Availability: The MATLAB implementation of the likelihood functions using the distribution assumptions is available at Bioinformatics online.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Differential-equation Models; T-distribution; Likelihood; Biology; Location
ISSN (print) / ISBN 1367-4803
e-ISSN 1367-4811
Journal Bioinformatics
Quellenangaben Volume: 33, Issue: 5, Pages: 1-8 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)