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.

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.

Citation

Vernon, I., Liu, J., Goldstein, M., Rowe, J., Topping, J., & Lindsey, K. (2018). Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions. BMC systems biology, 12, Article 1. https://doi.org/10.1186/s12918-017-0484-3

• 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
• Electronic ISSN: 1752-0509
• 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