Data reconciliation and parameter estimation in flux-balance analysis

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dc.contributor.authorRaghunathan, AU
dc.contributor.authorPerez Correa, JR
dc.contributor.authorBiegler, LT
dc.date.accessioned2024-01-10T13:12:09Z
dc.date.available2024-01-10T13:12:09Z
dc.date.issued2003
dc.description.abstractFlux balance analysis (FBA) has been shown to be a very effective tool to interpret and predict the metabolism of various microorganisms when the set of available measurements is not sufficient to determine the fluxes within the cell. In this methodology, an underdetermined stoichiometric model is solved using a linear programming (LP) approach. The predictions of FBA models can be improved if noisy measurements are checked for consistency, and these in turn are used to estimate model parameters. In this work, a formal methodology for data reconciliation and parameter estimation with underdetermined stoichiometric models is developed and assessed. The procedure is formulated as a nonlinear optimization problem, where the LP is transformed into a set of nonlinear constraints. However, some of these constraints violate standard regularity conditions, making the direct numerical solution very difficult. Hence, a barrier formulation is used to represent these constraints, and an iterative procedure is defined that allows solving the problem to the desired degree of convergence. This methodology is assessed using a stoichiometric yeast model. The procedure is used for data reconciliation where more reliable estimations of noisy measurements are computed. On the other hand, assuming unknown biomass composition, the procedure is applied for simultaneous data reconciliation and biomass composition estimation. In both cases it is verified that the minimum number of measurements required to get unbiased and reliable estimations is reduced if the LP approach is included as additional constraints in the optimization. (C) 2003 Wiley Periodicals, Inc.
dc.fechaingreso.objetodigital03-04-2024
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1002/bit.10823
dc.identifier.issn0006-3592
dc.identifier.pubmedidMEDLINE:14595782
dc.identifier.urihttps://doi.org/10.1002/bit.10823
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78146
dc.identifier.wosidWOS:000186384600009
dc.information.autorucIngeniería;Pérez J;S/I;100130
dc.issue.numero6
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final709
dc.pagina.inicio700
dc.publisherJOHN WILEY & SONS INC
dc.revistaBIOTECHNOLOGY AND BIOENGINEERING
dc.rightsacceso restringido
dc.subjectyeast
dc.subjectunderdetermined metabolic models
dc.subjectdata reconciliation
dc.subjectparameter estimation
dc.subjectMPEC
dc.subjectNLP
dc.subjectUNDERDETERMINED METABOLIC NETWORKS
dc.subjectLINEAR OPTIMIZATION
dc.subjectCONSTRAINTS
dc.subjectGROWTH
dc.subjectMODELS
dc.subjectERRORS
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleData reconciliation and parameter estimation in flux-balance analysis
dc.typeartículo
dc.volumen84
sipa.codpersvinculados100130
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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