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Title:
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Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization
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Author:
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Guillén Gosálbez, Gonzalo; Miró, Antoni; Alves, Rui; Sorribas Tello, Albert; Jiménez Esteller, Laureano
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Notes:
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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). |
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Subject(s):
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-Parameter estimation
-Structure identification
-Akaike criterion |
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Rights:
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cc-by, (c) Guillén Gosálbez et al., 2013
http://creativecommons.org/licenses/by/3.0/es/
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Document type:
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article
publishedVersion |
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Published by:
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BioMed Central
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Share:
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