Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization

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2013Author
Guillén Gosálbez, Gonzalo
Miró, Antoni
Jiménez Esteller, Laureano
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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.
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BMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11The following license files are associated with this item: