Show simple item record

dc.contributor.authorOguz, Cihanen_US
dc.contributor.authorLaomettachit, Teeraphanen_US
dc.contributor.authorChen, Katherine C.en_US
dc.contributor.authorWatson, Layne T.en_US
dc.contributor.authorBaumann, William T.en_US
dc.contributor.authorTyson, John J.en_US
dc.identifier.citationBMC Systems Biology. 2013 Jun 28;7(1):53
dc.description.abstractBackground '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.
dc.format.extent? - ? (17) page(s)en_US
dc.publisherBiomed Central Ltden_US
dc.rightsCreative Commons Attribution 4.0 International*
dc.subjectMathematical & Computational Biologyen_US
dc.subjectBudding Yeasten_US
dc.subjectCell Cycleen_US
dc.subjectODE Modelen_US
dc.subjectModel Reductionen_US
dc.subjectPhenotypic Constraintsen_US
dc.subjectLatin Hypercube Samplingen_US
dc.subjectDifferential Evolutionen_US
dc.subjectSensitivity Analysisen_US
dc.subjectPhenotype Competitionen_US
dc.titleOptimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle modelen_US
dc.typeArticle - Refereed
dc.description.versionPublished (Publication status)en_US
dc.rights.holderCihan Oguz et al.; licensee BioMed Central Ltd.
dc.title.serialBMC SYSTEMS BIOLOGYen_US
pubs.organisational-group/Virginia Tech
pubs.organisational-group/Virginia Tech/All T&R Faculty
pubs.organisational-group/Virginia Tech/Engineering
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Faculty
pubs.organisational-group/Virginia Tech/Engineering/Computer Science
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineering
pubs.organisational-group/Virginia Tech/Faculty of Health Sciences
pubs.organisational-group/Virginia Tech/Science
pubs.organisational-group/Virginia Tech/Science/Biological Sciences
pubs.organisational-group/Virginia Tech/Science/COS T&R Faculty
pubs.organisational-group/Virginia Tech/University Distinguished Professors

Files in this item


This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International