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dc.contributor.authorZhang, Weiruo
dc.contributor.authorKolte, Ritesh
dc.contributor.authorDill, David L.
dc.date.accessioned2021-11-03T07:15:43Z
dc.date.available2021-11-03T07:15:43Z
dc.date.issued2015
dc.identifier.citationZhang, W., Kolte, R., & Dill, D. L. (2015). Towards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach. BMC Systems Biology, 9(1). doi:10.1186/s12918-015-0214-7
dc.identifier.issn1752-0509
dc.identifier.pmid26437964
dc.identifier.doi10.1186/s12918-015-0214-7
dc.identifier.urihttp://hdl.handle.net/10754/673079
dc.description.abstractBackground: High-throughput assays such as mass spectrometry have opened up the possibility for large-scale in vivo measurements of the metabolome. This data could potentially be used to estimate kinetic parameters for many metabolic reactions. However, high-throughput in vivo measurements have special properties that are not taken into account in existing methods for estimating kinetic parameters, including significant relative errors in measurements of metabolite concentrations and reaction rates, and reactions with multiple substrates and products, which are sometimes reversible. A new method is needed to estimate kinetic parameters taking into account these factors. Results: A new method, InVEst (In Vivo Estimation), is described for estimating reaction kinetic parameters, which addresses the specific challenges of in vivo data. InVEst uses maximum likelihood estimation based on a model where all measurements have relative errors. Simulations show that InVEst produces accurate estimates for a reversible enzymatic reaction with multiple reactants and products, that estimated parameters can be used to predict the effects of genetic variants, and that InVEst is more accurate than general least squares and graphic methods on data with relative errors. InVEst uses the bootstrap method to evaluate the accuracy of its estimates. Conclusions: InVEst addresses several challenges of in vivo data, which are not taken into account by existing methods. When data have relative errors, InVEst produces more accurate and robust estimates. InVEst also provides useful information about estimation accuracy using bootstrapping. It has potential applications of quantifying the effects of genetic variants, inference of the target of a mutation or drug treatment and improving flux estimation.
dc.description.sponsorshipD.L.D. and W.Z. were supported by a King Abdullah University of Science and Technology (KAUST) research grant under the KAUST Stanford Academic Excellence Alliance program. R.K. was supported by Stanford Graduate Fellowship.
dc.publisherBMC
dc.relation.urlhttps://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-015-0214-7
dc.subjectRelative error
dc.subjectEnzymatic reaction
dc.subjectParameter estimation
dc.subjectMaximum likelihood
dc.subjectError-in-all-measurements
dc.subjectIn vivo data
dc.titleTowards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach
dc.typeArticle
dc.identifier.journalBMC SYSTEMS BIOLOGY
dc.identifier.pmcidPMC4595320
dc.identifier.wosutWOS:000362397100001
dc.contributor.institutionStanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
dc.contributor.institutionStanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
dc.identifier.volume9
dc.identifier.issue1
dc.identifier.eid2-s2.0-84942868656


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