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.
%0 Journal Article
%1 Gosalbez2013Identification
%A Gosalbez, Gonzalo G.
%A Miro, Antoni
%A Alves, Rui
%A Sorribas, Albert
%A Jimenez, Laureano
%D 2013
%J BMC Systems Biology
%K kinetic-parameters optimisation regulatory-networks
%N 1
%P 113+
%R 10.1186/1752-0509-7-113
%T Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization
%U http://dx.doi.org/10.1186/1752-0509-7-113
%V 7
%X 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.
@article{Gosalbez2013Identification,
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.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Gosalbez, Gonzalo G. and Miro, Antoni and Alves, Rui and Sorribas, Albert and Jimenez, Laureano},
biburl = {https://www.bibsonomy.org/bibtex/2e317d182a6dcbdbc695f1c72e7e591ab/karthikraman},
citeulike-article-id = {12743858},
citeulike-linkout-0 = {http://dx.doi.org/10.1186/1752-0509-7-113},
citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/24176044},
citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=24176044},
day = 31,
doi = {10.1186/1752-0509-7-113},
interhash = {3fcc70d0dbc40fb0a87f05f60d69b606},
intrahash = {e317d182a6dcbdbc695f1c72e7e591ab},
issn = {1752-0509},
journal = {BMC Systems Biology},
keywords = {kinetic-parameters optimisation regulatory-networks},
month = oct,
number = 1,
pages = {113+},
pmid = {24176044},
posted-at = {2013-11-04 10:34:21},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization},
url = {http://dx.doi.org/10.1186/1752-0509-7-113},
volume = 7,
year = 2013
}