title: Parameter estimation using Simulated Annealing for S-system models of biochemical networks.
abstract: BACKGROUND
  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 (SA) can be effectively used for estimating the parameters of S-systems from time-course biochemical data. We demonstrate our methods using three 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 Escherichia coli.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  Supplementary material is available at Bioinformatics online.
authors.affiliation: Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de
authors.forename: Orland R
authors.initials: OR
authors.lastname: Gonzalez
authors.forename: Christoph
authors.initials: C
authors.lastname: Küper
authors.forename: Kirsten
authors.initials: K
authors.lastname: Jung
authors.forename: Prospero C
authors.initials: PC
authors.lastname: Naval
authors.forename: Eduardo
authors.initials: E
authors.lastname: Mendoza
mesh_terms: D001671:Biochemistry / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
chemical_list: D020543:Proteome