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Stoichiometric and Constraint-Based Analysis of Biochemical Reaction Networks

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Abstract

Metabolic network analysis based on stoichiometric and constraint-based methods has become one of the most popular and successful modeling approaches in network and systems biology. Although these methods rely solely on the structure (stoichiometry) of metabolic networks and do not require extensive knowledge on mechanistic details of the involved reactions, they enable the extraction of important functional properties of biochemical reaction networks and deliver various testable predictions. This chapter gives an introduction on basic concepts and methods of stoichiometric and constraint-based modeling techniques. The mathematical foundations of the most important approaches—including graph-theoretical analysis, conservation relations, metabolic flux analysis, flux balance analysis, elementary modes, and minimal cut sets—will be presented, and applications in biology and biotechnology will be discussed. It will be shown that network problems arising in the context of metabolic network modeling are related to different fields of applied mathematics such as graph and hypergraph theory, linear algebra, linear programming, and combinatorial optimization. The methods presented herein are discussed in light of biological applications; however, most of them are generally applicable and useful to analyze any chemical or stoichiometric reaction network.

Keywords

  • Metabolic networks
  • Reaction networks
  • Stoichiometric models
  • Constraint-based modeling
  • Metabolic engineering
  • Systems biology

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Acknowledgement

This work was partially supported by the Federal State of Saxony-Anhalt (Research Center “Dynamic Systems: Biosystems Engineering”) and by the the German Federal Ministry of Education and Research (e:Bio project CYANOSYS II (FKZ 0316183D); Biotechnologie 2020+ project CASCO2 (FKZ: 031A180B)).

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Klamt, S., Hädicke, O., von Kamp, A. (2014). Stoichiometric and Constraint-Based Analysis of Biochemical Reaction Networks. In: Benner, P., Findeisen, R., Flockerzi, D., Reichl, U., Sundmacher, K. (eds) Large-Scale Networks in Engineering and Life Sciences. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-08437-4_5

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