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This article is part of the supplement: Selected articles from the 4th International Conference on Computational Systems Biology (ISB 2010)

Open Access Report

Two-stage flux balance analysis of metabolic networks for drug target identification

Zhenping Li1, Rui-Sheng Wang2 and Xiang-Sun Zhang3*

  • * Corresponding author: Xiang-Sun Zhang zxs@amt.ac.cn

  • † Equal contributors

Author Affiliations

1 School of Information, Beijing Wuzi University, Beijing 101149, China

2 Department of Physics, Pennsylvania State University, University Park, PA 16802, USA

3 Academy of Mathematics and Systems Science, CAS, Beijing 100190, China

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BMC Systems Biology 2011, 5(Suppl 1):S11  doi:10.1186/1752-0509-5-S1-S11

Published: 20 June 2011

Abstract

Background

Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.

Results

In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.

Conclusions

Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.