ArticleMetabolites2022
Inspecting the Solution Space of Genome-Scale Metabolic Models.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The FBA solution space kernel: introduction and illustrative examples.BMC bioinformatics · 2025Article
- Enhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs inBioinformatics advances · 2025Article
- Discretised Flux Balance Analysis for Reaction-Diffusion Simulation of Single-Cell Metabolism.Bulletin of mathematical biology · 2024Article
- Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance.PLoS computational biology · 2023Article
- FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models.Biomolecules · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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