title: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av,Països Catalans 26, 43007 Tarragona, Spain. gonzalo.guillen@urv.cat.
authors.forename: Gonzalo
authors.initials: G
authors.lastname: Guillén-Gosálbez
authors.forename: Antoni
authors.initials: A
authors.lastname: Miró
authors.forename: Rui
authors.initials: R
authors.lastname: Alves
authors.forename: Albert
authors.initials: A
authors.lastname: Sorribas
authors.forename: Laureano
authors.initials: L
authors.lastname: Jiménez
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D013997:Time Factors