A Duality-Based Method for Identifying Elemental Balance Violations in Metabolic Network Models

Authors Hooman Zabeti, Tamon Stephen, Bonnie Berger, Leonid Chindelevitch

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Hooman Zabeti
  • School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
Tamon Stephen
  • Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
Bonnie Berger
  • Department of Mathematics and CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States of America.
Leonid Chindelevitch
  • School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

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Hooman Zabeti, Tamon Stephen, Bonnie Berger, and Leonid Chindelevitch. A Duality-Based Method for Identifying Elemental Balance Violations in Metabolic Network Models. In 18th International Workshop on Algorithms in Bioinformatics (WABI 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 113, pp. 1:1-1:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Elemental balance, the property of having the same number of each type of atom on both sides of the equation, is a fundamental feature of chemical reactions. In metabolic network models, this property is typically verified on a reaction-by-reaction basis. In this paper we show how violations of elemental balance can be efficiently detected in an entire network, without the need for specifying the chemical formula of each of the metabolites, which enhances a modeler's ability to automatically verify that their model satisfies elemental balance. Our method makes use of duality theory, linear programming, and mixed integer linear programming, and runs efficiently on genome-scale metabolic networks (GSMNs). We detect elemental balance violations in 40 out of 84 metabolic network models in the BiGG database. We also identify a short list of reactions that are candidates for being elementally imbalanced. Out of these candidates, nearly half turn out to be truly imbalanced reactions, and the rest can be seen as witnesses of elemental balance violations elsewhere in the network. The majority of these violations involve a proton imbalance, a known challenge of metabolic network reconstruction. Our approach is efficient, easy to use and powerful. It can be helpful to metabolic network modelers during model verification. Our methods are fully integrated into the MONGOOSE software suite and are available at https://github.com/WGS-TB/MongooseGUI3.

Subject Classification

ACM Subject Classification
  • Applied computing → Biological networks
  • Metabolic network analysis
  • elemental imbalance
  • linear programming
  • model verification


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