OpenSSL SSL_connect: Connection reset by peer in connection to v2.sherpa.ac.uk:443 PuSH - Publication Server of Helmholtz Zentrum München: Scalable inference of ordinary differential equation models of biochemical processes.

PuSH - Publication Server of Helmholtz Zentrum München

Fröhlich, F. ; Loos, C. ; Hasenauer, J.

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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Large-scale Models ; Ordinary Differential Equations ; Parameter Estimation ; Uncertainty Analysis
ISSN (print) / ISBN 1064-3745
e-ISSN 1940-6029
Book Volume Title Protocol
Journal Methods in Molecular Biology
Quellenangaben Volume: 1883, Issue: , Pages: 385-422 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)