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Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions

Vernon, I.; Liu, J.; Goldstein, M.; Rowe, J.; Topping, J.; Lindsey, K.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions Thumbnail


Authors

M. Goldstein

J. Rowe



Abstract

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology.

Journal Article Type Article
Acceptance Date Nov 9, 2017
Online Publication Date Jan 2, 2018
Publication Date Jan 2, 2018
Deposit Date Jan 19, 2016
Publicly Available Date Nov 20, 2017
Journal BMC Systems Biology
Publisher BioMed Central
Peer Reviewed Peer Reviewed
Volume 12
Article Number 1
DOI https://doi.org/10.1186/s12918-017-0484-3
Public URL https://durham-repository.worktribe.com/output/1422221
Related Public URLs https://arxiv.org/abs/1607.06358

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Accepted Journal Article (6.6 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.






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