Parameter estimation for asymptotic regression model by particle swarm optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Inference of genetic networks using linear programming machines: application of a priori knowledge
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A parameter estimation approach for non-linear systems biology models using spline approximation
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Component-based construction of bio-pathway models: The parameter estimation problem
Theoretical Computer Science
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Smooth functional tempering for nonlinear differential equation models
Statistics and Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiscale Denoising of Biological Data: A Comparative Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
State and parameter estimation for nonlinear biological phenomena modeled by S-systems
Digital Signal Processing
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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 (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. 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. Contact: gonzalez@bio.ifi.lmu.de Supplementary information: Supplementary material is available at Bioinformatics online.