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Statistical inference in ensemble modeling of cellular metabolism

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic mod...

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Autores principales: Hameri, Tuure, Boldi, Marc-Olivier, Hatzimanikatis, Vassily
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922442/
https://www.ncbi.nlm.nih.gov/pubmed/31815929
http://dx.doi.org/10.1371/journal.pcbi.1007536
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author Hameri, Tuure
Boldi, Marc-Olivier
Hatzimanikatis, Vassily
author_facet Hameri, Tuure
Boldi, Marc-Olivier
Hatzimanikatis, Vassily
author_sort Hameri, Tuure
collection PubMed
description Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size.
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spelling pubmed-69224422020-01-07 Statistical inference in ensemble modeling of cellular metabolism Hameri, Tuure Boldi, Marc-Olivier Hatzimanikatis, Vassily PLoS Comput Biol Research Article Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size. Public Library of Science 2019-12-09 /pmc/articles/PMC6922442/ /pubmed/31815929 http://dx.doi.org/10.1371/journal.pcbi.1007536 Text en © 2019 Hameri et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Hameri, Tuure
Boldi, Marc-Olivier
Hatzimanikatis, Vassily
Statistical inference in ensemble modeling of cellular metabolism
title Statistical inference in ensemble modeling of cellular metabolism
title_full Statistical inference in ensemble modeling of cellular metabolism
title_fullStr Statistical inference in ensemble modeling of cellular metabolism
title_full_unstemmed Statistical inference in ensemble modeling of cellular metabolism
title_short Statistical inference in ensemble modeling of cellular metabolism
title_sort statistical inference in ensemble modeling of cellular metabolism
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922442/
https://www.ncbi.nlm.nih.gov/pubmed/31815929
http://dx.doi.org/10.1371/journal.pcbi.1007536
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