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Related Experiment Videos

Algorithm for perturbing thermodynamically infeasible metabolic networks.

R Nigam1, S Liang

  • 1W.W. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305-4085, USA. rakesh@quake.stanford.edu

Computers in Biology and Medicine
|April 18, 2006
PubMed
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This study introduces an algorithm to identify and resolve thermodynamically infeasible cycles in metabolic networks. The method modifies network topology while maintaining mass balance, applied here to Escherichia coli.

Area of Science:

  • Metabolic engineering
  • Systems biology
  • Biochemical network analysis

Background:

  • Cellular reaction networks adhere to mass and energy balance laws.
  • Thermodynamics, specifically the second law, imposes topological constraints on metabolic networks.
  • Certain reaction subnetworks can violate thermodynamic principles when isolated.

Purpose of the Study:

  • To develop an algorithm for identifying thermodynamically infeasible subnetworks.
  • To propose a method for resolving these infeasibilities by altering network topology.
  • To ensure the preservation of mass balance optimality in modified networks.

Main Methods:

  • An algorithm was developed to detect subnetworks violating the second law of thermodynamics.
  • Infeasible reactions within these subnetworks were identified for deletion.

Related Experiment Videos

  • The algorithm was applied to the complete metabolic network of Escherichia coli.

    • Main Results:

    • The algorithm successfully identified thermodynamically infeasible subnetworks.
    • Deletion of specific reactions rendered these subnetworks feasible.
    • Network topology was perturbed, but mass balance optimality was maintained.
    • The method demonstrated applicability on a large-scale Escherichia coli metabolic model.

    Conclusions:

    • The developed algorithm effectively addresses thermodynamic constraints in metabolic networks.
    • This approach offers a way to refine metabolic models for improved biological realism.
    • The method preserves essential properties like mass balance while improving network feasibility.