000162819 001__ 162819
000162819 005__ 20251017144648.0
000162819 0247_ $$2doi$$a10.1093/bioadv/vbaf166
000162819 0248_ $$2sideral$$a145368
000162819 037__ $$aART-2025-145368
000162819 041__ $$aeng
000162819 100__ $$aLázaro, Jorge$$uUniversidad de Zaragoza
000162819 245__ $$aEnhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs in <i>Escherichia coli</i>
000162819 260__ $$c2025
000162819 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162819 5203_ $$aMetabolic models are valuable tools for analyzing and predicting cellular features such as growth, gene essentiality, and product formation. Among the various types of metabolic models, two prominent categories are constraint-based models and kinetic models. Constraint-based models typically represent a large subset of an organism’s metabolic reactions and incorporate reaction stoichiometry, gene regulation, and constant flux bounds. However, their analyses are restricted to steady-state conditions, making it difficult to optimize competing objective functions. In contrast, kinetic models offer detailed kinetic information but are limited to a smaller subset of metabolic reactions, providing precise predictions for only a fraction of an organism’s metabolism. To address these limitations, we proposed a hybrid approach that integrates these modeling frameworks by redefining the flux bounds in genome-scale constraint-based models using kinetic data. We applied this method to the constraint-based model of Escherichia coli, examining both its wild-type form and a genetically modified strain engineered for citramalate production. Our results demonstrate that the enriched model achieves more realistic reaction flux boundaries. Furthermore, by fixing the growth rate to a value derived from kinetic information, we resolved a flux bifurcation between growth and citramalate production in the modified strain, enabling accurate predictions of citramalate production rates.
000162819 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130449B-I00$$9info:eu-repo/grantAgreement/ES/DGA/T21-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00
000162819 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162819 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162819 700__ $$aWongprommoon, Arin
000162819 700__ $$0(orcid)0000-0002-7093-228X$$aJúlvez, Jorge$$uUniversidad de Zaragoza
000162819 700__ $$aOliver, Stephen G
000162819 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000162819 773__ $$g5, 1 (2025), [11 p]$$pBioinform. adv.$$tBioinformatics advances$$x2635-0041
000162819 8564_ $$s1371073$$uhttps://zaguan.unizar.es/record/162819/files/texto_completo.pdf$$yVersión publicada
000162819 8564_ $$s3134954$$uhttps://zaguan.unizar.es/record/162819/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162819 909CO $$ooai:zaguan.unizar.es:162819$$particulos$$pdriver
000162819 951__ $$a2025-10-17-14:35:06
000162819 980__ $$aARTICLE