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Bioinformatics Advance Access published online on October 11, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl522
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 6, 2006
Revised September 13, 2006
Accepted September 21, 2006

Article

Parameter estimation using Simulated Annealing for S-system models of biochemical networks

Orland R. Gonzalez 1 *, Christoph Küper 2, Kirsten Jung 3, Prospero C. Naval Jr. 1, and Eduardo Mendoza 4

1 Department of Computer Science University of the Philippines-Diliman
2 Department Biologie I, Bereich Mikrobiologie, Ludwig-Maximilians-Universität; Medizinische Fakultät, Physiologisches Institut, Ludwig-Maximilians-Universität
3 Department Biologie I, Bereich Mikrobiologie, Ludwig-Maximilians-Universität
4 Mathematics Department University of the Philippines-Diliman; Physics Department & Center for NanoScience Ludwig-Maximilians-University Munich

* To whom correspondence should be addressed.
Orland R. Gonzalez, E-mail: gonzalez{at}bio.ifi.lmu.de


  Abstract

Motivation: High-throughput technologies now allow the acquisition of biological data such as comprehensive biochemical time-courses at unprecedented rates. These temporal profiles carry topological and kinetic information regarding the biochemical network from which they were drawn. Retrieving this information will require systematic application of both experimental and computational methods.

Results: S-systems are non-linear mathematical approximative models based on the power-law formalism. They provide a general framework for the simulation of integrated biological systems exhibiting complex dynamics such as genetic circuits, signal transduction and metabolic networks. We describe how the heuristic optimization technique Simulated Annealing can be effectively used for estimating the parameters of S-systems from time-course biochemical data. We demonstrate our methods using 3 artificial networks designed to simulate different network topologies and behavior. We then end with an application to a real biochemical network by creating a working model for the cadBA system in E. coli.

Availability: The source code written in C++ is available at http://www.engg.upd.edu.ph/~naval/bioinformcode.html. All the necessary programs including the required compiler are described in a document archived with the source code.

Associate Editor: Martin Bishop
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