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Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these mod...

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Autores principales: Bernstein, David B., Sulheim, Snorre, Almaas, Eivind, Segrè, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890832/
https://www.ncbi.nlm.nih.gov/pubmed/33602294
http://dx.doi.org/10.1186/s13059-021-02289-z
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author Bernstein, David B.
Sulheim, Snorre
Almaas, Eivind
Segrè, Daniel
author_facet Bernstein, David B.
Sulheim, Snorre
Almaas, Eivind
Segrè, Daniel
author_sort Bernstein, David B.
collection PubMed
description The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02289-z.
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spelling pubmed-78908322021-02-22 Addressing uncertainty in genome-scale metabolic model reconstruction and analysis Bernstein, David B. Sulheim, Snorre Almaas, Eivind Segrè, Daniel Genome Biol Review The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02289-z. BioMed Central 2021-02-18 /pmc/articles/PMC7890832/ /pubmed/33602294 http://dx.doi.org/10.1186/s13059-021-02289-z Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Bernstein, David B.
Sulheim, Snorre
Almaas, Eivind
Segrè, Daniel
Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title_full Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title_fullStr Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title_full_unstemmed Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title_short Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
title_sort addressing uncertainty in genome-scale metabolic model reconstruction and analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890832/
https://www.ncbi.nlm.nih.gov/pubmed/33602294
http://dx.doi.org/10.1186/s13059-021-02289-z
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