Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models

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doi doi:10.2390/biecoll-jib-2010-133
submission February 18, 2010
published March 25, 2010
NCBI PubMed PubMed ID 20375457

Syed M Baker, Kai Schallau and Björn H Junker

Correspondence should be addressed to:
Syed Baker
Systems Biology, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben 06466, Germany
ed.nebelsretag-kpi@nullrekab


Abstract

Computational models in systems biology are usually characterized by a lack of reliable parameter values. This is especially true for kinetic metabolic models. Experimental data can be used to estimate these missing parameters. Different optimization techniques have been explored to solve this challenging task but none has proved to be superior to the other. In this paper we review the problem of parameter estimation in kinetic models. We focus on the suitability of four commonly used optimization techniques of parameter estimation in biochemical pathways and make a comparison between those methods. The suitability of each technique is evaluated based on the ability of converging to a solution within a reasonable amount of time. As most local optimization methods fail to arrive at a satisfactory solution we only considered the global optimization techniques. A case study of the upper part of Glycolysis consisting 15 parameters is taken as the benchmark model for evaluating these methods.

Reference

Syed M Baker, Kai Schallau and Björn H Junker. Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models. Journal of Integrative Bioinformatics, 7(3):133, 2010. Online Journal: http://journal.imbio.de/index.php?paper_id=133
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