Bioinformatics Advance Access originally published online on June 22, 2007
Bioinformatics 2007 23(17):2281-2289; doi:10.1093/bioinformatics/btm326
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Detection of potential enzyme targets by metabolic modelling and optimization: Application to a simple enzymopathy
1Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany, 2Grupo de Tecnología Bioquímica, Department of Biochemistry and Molecular Biology, University of La Laguna, Astrofisico Francisco Sanchez s/n, 38206, 3Instituto Canario de Investigación del Cáncer (ICIC), Canary Islands, 4University School of Experimental Sciences and Technology (EUCET), Universitat Internacional de Catalunya, Nova estacio s/n, 43500 and 5Department of Biochemistry and Molecular Biology, Faculty of Chemistry and CERQT-Parc Cientific de Barcelona (PCB), University of Barcelona, Marti i Franques 1, 08028, Spain
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: A very promising approach in drug discovery involves the integration of available biomedical data through mathematical modelling and data mining. We have developed a method called optimization program for drug discovery (OPDD) that allows new enzyme targets to be identified in enzymopathies through the integration of metabolic models and biomedical data in a mathematical optimization program. The method involves four steps: (i) collection of the necessary information about the metabolic system and disease; (ii) translation of the information into mathematical terms; (iii) computation of the optimization programs prioritizing the solutions that propose the inhibition of a reduced number of enzymes and (iv) application of additional biomedical criteria to select and classify the solutions. Each solution consists of a set of predicted values for metabolites, initial substrates and enzyme activities, which describe a biologically acceptable steady state of the system that shifts the pathologic state towards a healthy state.
Results: The OPDD was used to detect target enzymes in an enzymopathy, the human hyperuricemia. An existing S-system model and bibliographic information about the disease were used. The method detected six single-target enzyme solutions involving dietary modification, one of them coinciding with the conventional clinical treatment using allopurinol. The OPDD detected a large number of possible solutions involving two enzyme targets. All except one contained one of the previously detected six enzyme targets. The purpose of this work was not to obtain solutions for direct clinical implementation but to illustrate how increasing levels of biomedical information can be integrated together with mathematical models in drug discovery.
Contact: julio.vera{at}informartik.uni-rostock.de or julio_vera_g{at}yahoo.es
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Thomas Lengauer
Received on May 2, 2006; revised on May 3, 2007; accepted on June 16, 2007
CiteULike 
Connotea 
Del.icio.us What's this?