Cargando…

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growt...

Descripción completa

Detalles Bibliográficos
Autores principales: Schulz, Christian, Kumelj, Tjasa, Karlsen, Emil, Almaas, Eivind
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177628/
https://www.ncbi.nlm.nih.gov/pubmed/34029317
http://dx.doi.org/10.1371/journal.pcbi.1008528
_version_ 1783703421851795456
author Schulz, Christian
Kumelj, Tjasa
Karlsen, Emil
Almaas, Eivind
author_facet Schulz, Christian
Kumelj, Tjasa
Karlsen, Emil
Almaas, Eivind
author_sort Schulz, Christian
collection PubMed
description Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.
format Online
Article
Text
id pubmed-8177628
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-81776282021-06-07 Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition Schulz, Christian Kumelj, Tjasa Karlsen, Emil Almaas, Eivind PLoS Comput Biol Research Article Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments. Public Library of Science 2021-05-24 /pmc/articles/PMC8177628/ /pubmed/34029317 http://dx.doi.org/10.1371/journal.pcbi.1008528 Text en © 2021 Schulz et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Schulz, Christian
Kumelj, Tjasa
Karlsen, Emil
Almaas, Eivind
Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title_full Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title_fullStr Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title_full_unstemmed Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title_short Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
title_sort genome-scale metabolic modelling when changes in environmental conditions affect biomass composition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177628/
https://www.ncbi.nlm.nih.gov/pubmed/34029317
http://dx.doi.org/10.1371/journal.pcbi.1008528
work_keys_str_mv AT schulzchristian genomescalemetabolicmodellingwhenchangesinenvironmentalconditionsaffectbiomasscomposition
AT kumeljtjasa genomescalemetabolicmodellingwhenchangesinenvironmentalconditionsaffectbiomasscomposition
AT karlsenemil genomescalemetabolicmodellingwhenchangesinenvironmentalconditionsaffectbiomasscomposition
AT almaaseivind genomescalemetabolicmodellingwhenchangesinenvironmentalconditionsaffectbiomasscomposition