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Scalable inference of ordinary differential equation models of biochemical processes.

Methods Mol. Biol. 1883, 385-422 (2019)
Postprint DOI
Open Access Green
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Large-scale Models ; Ordinary Differential Equations ; Parameter Estimation ; Uncertainty Analysis
ISSN (print) / ISBN 1064-3745
e-ISSN 1940-6029
Bandtitel Protocol
Quellenangaben Band: 1883, Heft: , Seiten: 385-422 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Begutachtungsstatus Peer reviewed