dc.contributor.author |
Guillén Gosálbez, Gonzalo |
dc.contributor.author |
Miró, Antoni |
dc.contributor.author |
Alves, Rui |
dc.contributor.author |
Sorribas Tello, Albert |
dc.contributor.author |
Jiménez Esteller, Laureano |
dc.date |
2015-06-02T11:05:51Z |
dc.date |
2015-06-02T11:05:51Z |
dc.date |
2013 |
dc.identifier |
1752-0509 |
dc.identifier |
http://hdl.handle.net/10459.1/48285 |
dc.identifier |
https://doi.org/10.1186/1752-0509-7-113 |
dc.identifier.uri |
http://hdl.handle.net/10459.1/48285 |
dc.description |
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. |
dc.description |
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). |
dc.language |
eng |
dc.publisher |
BioMed Central |
dc.relation |
MICINN/PN2008-2011/CTQ2009-14420-C02 |
dc.relation |
MICINN/PN2008-2011/CTQ2012-37039-C02 |
dc.relation |
MICINN/PN2008-2011/DPI2012-37154-C02-02 |
dc.relation |
MICINN/PN2008-2011/BFU2008-00196/BMC |
dc.relation |
MICINN/PN2008-2011/BFU2010-17704 |
dc.relation |
Reproducció del document publicat a https://doi.org/10.1186/1752-0509-7-113 |
dc.relation |
BMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11 |
dc.relation |
info:eu-repo/grantAgreement/EC/FP7/251298 |
dc.rights |
cc-by, (c) Guillén Gosálbez et al., 2013 |
dc.rights |
http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Parameter estimation |
dc.subject |
Structure identification |
dc.subject |
Akaike criterion |
dc.title |
Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization |
dc.type |
article |
dc.type |
publishedVersion |