Inspecting the Solution Space of Genome-Scale Metabolic Models

Seyed Babak Loghmani, Nadine Veith, Sven Sahle, Frank T. Bergmann, Brett G. Olivier, Ursula Kummer*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Original languageEnglish
Article number43
Pages (from-to)1-20
Number of pages20
Issue number1
Early online date5 Jan 2022
Publication statusPublished - Jan 2022

Bibliographical note

Special Issue: Genome-Scale Metabolic Models.

Funding Information:
Funding: S.B.L. would like to thank the HGS MathComp and the Graduate academy at Heidelberg University for financial support. N.V. thanks the DFG (grant numbers VE1075/2-1 and FI 585 1588/ 2-1) for funding.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Constraints
  • FBA
  • FVA
  • Perturbation
  • Robustness
  • Sensitivity
  • Solution space


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