Paper Info
Title
Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization.
Abstract
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.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.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.
Year
DOI
Venue
2013
10.1186/1752-0509-7-113
BMC systems biology
Keywords
Field
DocType
parameter estimation,algorithms,computational biology
Integer,Computer science,Orthogonal collocation,Systems biology,Network topology,Estimation theory,Bioinformatics,Generality,Kinetic energy
Journal
Volume
Issue
ISSN
7
1
1752-0509
Citations 
PageRank 
References 
1
0.35
11
Authors
5
Name
Order
Citations
PageRank
Gonzalo Guillén-Gosálbez118920.22
Antoni Miró2130.92
Rui Alves319632.99
Albert Sorribas4667.81
Laureano Jiménez5729.11