dc.contributor.author	Oguz, Cihan	en
dc.contributor.author	Laomettachit, Teeraphan	en
dc.contributor.author	Chen, Katherine C.	en
dc.contributor.author	Watson, Layne T.	en
dc.contributor.author	Baumann, William T.	en
dc.contributor.author	Tyson, John J.	en
dc.contributor.department	Electrical and Computer Engineering	en
dc.contributor.department	Biological Sciences	en
dc.contributor.department	Computer Science	en
dc.contributor.department	Mathematics	en
dc.date.accessioned	2016-12-09T21:40:26Z	en
dc.date.available	2016-12-09T21:40:26Z	en
dc.date.issued	2013-06-28	en
dc.description.abstract	Background 'Parameter estimation from experimental data is critical for mathematical modeling of protein regulatory networks. For realistic networks with dozens of species and reactions, parameter estimation is an especially challenging task. In this study, we present an approach for parameter estimation that is effective in fitting a model of the budding yeast cell cycle (comprising 26 nonlinear ordinary differential equations containing 126 rate constants) to the experimentally observed phenotypes (viable or inviable) of 119 genetic strains carrying mutations of cell cycle genes. Results Starting from an initial guess of the parameter values, which correctly captures the phenotypes of only 72 genetic strains, our parameter estimation algorithm quickly improves the success rate of the model to 105-111 of the 119 strains. This success rate is comparable to the best values achieved by a skilled modeler manually choosing parameters over many weeks. The algorithm combines two search and optimization strategies. First, we use Latin hypercube sampling to explore a region surrounding the initial guess. From these samples, we choose ∼20 different sets of parameter values that correctly capture wild type viability. These sets form the starting generation of differential evolution that selects new parameter values that perform better in terms of their success rate in capturing phenotypes. In addition to producing highly successful combinations of parameter values, we analyze the results to determine the parameters that are most critical for matching experimental outcomes and the most competitive strains whose correct outcome with a given parameter vector forces numerous other strains to have incorrect outcomes. These “most critical parameters” and “most competitive strains” provide biological insights into the model. Conversely, the “least critical parameters” and “least competitive strains” suggest ways to reduce the computational complexity of the optimization. Conclusions Our approach proves to be a useful tool to help systems biologists fit complex dynamical models to large experimental datasets. In the process of fitting the model to the data, the tool identifies suggestive correlations among aspects of the model and the data.	en
dc.description.version	Published version	en
dc.format.extent	? - ? (17) page(s)	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	BMC Systems Biology. 2013 Jun 28;7(1):53	en
dc.identifier.doi	https://doi.org/10.1186/1752-0509-7-53	en
dc.identifier.issn	1752-0509	en
dc.identifier.uri	http://hdl.handle.net/10919/73641	en
dc.identifier.volume	7	en
dc.language.iso	en	en
dc.publisher	Biomed Central	en
dc.relation.uri	http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000321338300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.holder	Cihan Oguz et al.; licensee BioMed Central Ltd.	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.subject	Mathematical & Computational Biology	en
dc.subject	Optimization	en
dc.subject	Budding Yeast	en
dc.subject	Cell Cycle	en
dc.subject	ODE Model	en
dc.subject	Model Reduction	en
dc.subject	Phenotypic Constraints	en
dc.subject	Latin Hypercube Sampling	en
dc.subject	Differential Evolution	en
dc.subject	Sensitivity Analysis	en
dc.subject	Phenotype Competition	en
dc.subject	SYSTEMS BIOLOGY	en
dc.subject	SACCHAROMYCES-CEREVISIAE	en
dc.subject	BIOCHEMICAL NETWORKS	en
dc.subject	SENSITIVITY-ANALYSIS	en
dc.subject	EXPERIMENTAL-DESIGN	en
dc.subject	ROBUSTNESS	en
dc.subject	IDENTIFIABILITY	en
dc.subject	MECHANISM	en
dc.title	Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model	en
dc.title.serial	BMC Systems Biology	en
dc.type	Article - Refereed	en
dc.type.dcmitype	Text	en
pubs.organisational-group	/Virginia Tech	en
pubs.organisational-group	/Virginia Tech/All T&R Faculty	en
pubs.organisational-group	/Virginia Tech/Engineering	en
pubs.organisational-group	/Virginia Tech/Engineering/COE T&R Faculty	en
pubs.organisational-group	/Virginia Tech/Engineering/Computer Science	en
pubs.organisational-group	/Virginia Tech/Engineering/Electrical and Computer Engineering	en
pubs.organisational-group	/Virginia Tech/Faculty of Health Sciences	en
pubs.organisational-group	/Virginia Tech/Science	en
pubs.organisational-group	/Virginia Tech/Science/Biological Sciences	en
pubs.organisational-group	/Virginia Tech/Science/COS T&R Faculty	en
pubs.organisational-group	/Virginia Tech/University Distinguished Professors	en

Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model

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