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dc.contributor.authorSpencer, Sabrina Leigh
dc.contributor.authorAlbeck, John Gerald
dc.contributor.authorBurke, John M.
dc.contributor.authorSorger, Peter Karl
dc.contributor.authorGaudet, Suzanne
dc.contributor.authorKim, Kyoung Ae
dc.contributor.authorKim, Do Hyun
dc.date.accessioned2013-01-04T20:53:08Z
dc.date.issued2010
dc.identifier.citationKim, Kyoung A., Sabrina L. Spencer, John G. Albeck, John M. Burke, Peter K. Sorger, Suzanne Gaudet, and Do H. Kim. 2010. Systematic calibration of a cell signaling network model. BMC Bioinformatics 11:202.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10139270
dc.description.abstractBackground: Mathematical modeling is being applied to increasingly complex biological systems and datasets; however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death. Results: We propose a serial framework integrating analysis and calibration modules and we compare various methods for global sensitivity analysis and global parameter estimation. First, adequacy of the network structure is checked by global sensitivity analysis to changes in concentrations of molecular species, validating that the model can reproduce qualitative features of the system behavior derived from experiments or literature surveys. Second, rate parameters are ranked by importance using gradient-based and variance-based sensitivity indices, and we systematically determine the optimal number of parameters to include in model calibration. Third, deterministic, stochastic and hybrid algorithms for global optimization are applied to estimate the values of the most important parameters by fitting to time series data. We compare the performance of these three optimization algorithms. Conclusions: Our proposed framework covers the entire process from validating a proto-model to establishing a realistic model for in silico experiments and thereby provides a generalized workflow for the construction of predictive models of complex network systems.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi: 10.1186/1471-2105-11-202en_US
dc.relation.hasversionhttp://www.biomedcentral.com/1471-2105/11/202en_US
dash.licenseLAA
dc.titleSystemic Calibration of a Cell Signaling Network Modelen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Bioinformaticsen_US
dash.depositing.authorSpencer, Sabrina Leigh
dc.date.available2013-01-04T20:53:08Z
dash.affiliation.otherHMS^Systems Biologyen_US
dc.identifier.doi10.1186/1471-2105-11-202*
dash.authorsorderedfalse
dash.contributor.affiliatedAlbeck, John Gerald
dash.contributor.affiliatedSpencer, Sabrina Leigh
dash.contributor.affiliatedSorger, Peter
dash.contributor.affiliatedGaudet, Suzanne


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