Inspecting the solution space of genome-scale metabolic models

• Verfasst von: Loghmani, Seyed Babak [VerfasserIn]
• Verfasst von: Veith, Nadine [VerfasserIn]
• Verfasst von: Sahle, Sven [VerfasserIn]
• Verfasst von: Bergmann, Frank T. [VerfasserIn]
• Verfasst von: Olivier, Brett G. [VerfasserIn]
• Verfasst von: Kummer, Ursula [VerfasserIn]
• Titel: Inspecting the solution space of genome-scale metabolic models
• Verf.angabe: Seyed Babak Loghmani, Nadine Veith, Sven Sahle, Frank T. Bergmann, Brett G. Olivier and Ursula Kummer
• E-Jahr: 2022
• Jahr: 5 January 2022
• Umfang: 20 S.
• Fussnoten: Gesehen am 05.05.2022
• Titel Quelle: Enthalten in: Metabolites
• Ort Quelle: Basel : MDPI, 2011
• Jahr Quelle: 2022
• Band/Heft Quelle: 12(2022), 1, Artikel-ID 43, Seite 1-20
• ISSN Quelle: 2218-1989
• DOI: doi:10.3390/metabo12010043
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: constraints
• Sach-SW: FBA
• Sach-SW: FVA
• Sach-SW: perturbation
• Sach-SW: robustness
• Sach-SW: sensitivity
• Sach-SW: solution space
• K10plus-PPN: 1801067317

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.