2026-04-14T03:11:35Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/482852024-12-05T22:28:46Zcom_2072_3622col_2072_479130

Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization Guillén Gosálbez, Gonzalo Miró, Antoni Alves, Rui Sorribas Tello, Albert Jiménez Esteller, Laureano Parameter estimation Structure identification Akaike criterion Background: Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results: Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixedinteger dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion: The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters. The authors acknowledges the financial support of the following institutions: Spanish Ministry of Education and Science (CTQ2009-14420-C02, CTQ2012-37039-C02, DPI2012-37154-C02-02, BFU2008-00196/BMC, BFU2010-17704, SGR2009-0809 and ENE 2011-28269-CO3-03), Spanish Ministry of External Affairs (projects PHB 2008-0090-PC), and European Commission (Marie Curie Actions - IAPP program - FP7/251298). 2015-06-02T11:05:51Z 2015-06-02T11:05:51Z 2013 article publishedVersion https://doi.org/10.1186/1752-0509-7-113 1752-0509 http://hdl.handle.net/10459.1/48285 http://hdl.handle.net/10459.1/48285 eng MICINN/PN2008-2011/CTQ2009-14420-C02 MICINN/PN2008-2011/CTQ2012-37039-C02 MICINN/PN2008-2011/DPI2012-37154-C02-02 MICINN/PN2008-2011/BFU2008-00196/BMC MICINN/PN2008-2011/BFU2010-17704 Reproducció del document publicat a https://doi.org/10.1186/1752-0509-7-113 BMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11 info:eu-repo/grantAgreement/EC/FP7/251298 cc-by, (c) Guillén Gosálbez et al., 2013 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/es/ BioMed Central