Dataset Information

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

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 mixed-integer 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.

SUBMITTER: Guillen-Gosalbez G

PROVIDER: S-EPMC3832746 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Guillén-Gosálbez Gonzalo G Miró Antoni A Alves Rui R Sorribas Albert A Jiménez Laureano L

BMC systems biology 20131031

<h4>Background</h4>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.<h4>Results</h4>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 mixed-integer dynamic optimization problem with: (i) ...[more]

PMID: 24176044

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Dataset Information

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

Background

Results

Conclusion

Publications

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

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