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

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2013
Author
Guillén Gosálbez, Gonzalo
Miró, Antoni
Alves, Rui
Sorribas Tello, Albert
Jiménez Esteller, Laureano
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Guillén Gosálbez, Gonzalo; Miró, Antoni; Alves, Rui; Sorribas Tello, Albert; Jiménez Esteller, Laureano; . (2013) . Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization. BMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11. https://doi.org/10.1186/1752-0509-7-113.
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Abstract
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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http://hdl.handle.net/10459.1/48285
DOI
https://doi.org/10.1186/1752-0509-7-113
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BMC Systems Biology, 2013, vol. 7, núm. 113, p. 1-11
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Except where otherwise noted, this item's license is described as cc-by, (c) Guillén Gosálbez et al., 2013