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Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration...

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Autores principales: Galvão Ferrarini, Mariana, Ziska, Irene, Andrade, Ricardo, Julien-Laferrière, Alice, Duchemin, Louis, César, Roberto Marcondes, Mary, Arnaud, Vinga, Susana, Sagot, Marie-France
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905348/
https://www.ncbi.nlm.nih.gov/pubmed/35281848
http://dx.doi.org/10.3389/fgene.2022.815476
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author Galvão Ferrarini, Mariana
Ziska, Irene
Andrade, Ricardo
Julien-Laferrière, Alice
Duchemin, Louis
César, Roberto Marcondes
Mary, Arnaud
Vinga, Susana
Sagot, Marie-France
author_facet Galvão Ferrarini, Mariana
Ziska, Irene
Andrade, Ricardo
Julien-Laferrière, Alice
Duchemin, Louis
César, Roberto Marcondes
Mary, Arnaud
Vinga, Susana
Sagot, Marie-France
author_sort Galvão Ferrarini, Mariana
collection PubMed
description Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.
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spelling pubmed-89053482022-03-10 Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations Galvão Ferrarini, Mariana Ziska, Irene Andrade, Ricardo Julien-Laferrière, Alice Duchemin, Louis César, Roberto Marcondes Mary, Arnaud Vinga, Susana Sagot, Marie-France Front Genet Genetics Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes. Frontiers Media S.A. 2022-02-21 /pmc/articles/PMC8905348/ /pubmed/35281848 http://dx.doi.org/10.3389/fgene.2022.815476 Text en Copyright © 2022 Galvão Ferrarini, Ziska, Andrade, Julien-Laferrière, Duchemin, César, Mary, Vinga and Sagot. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Galvão Ferrarini, Mariana
Ziska, Irene
Andrade, Ricardo
Julien-Laferrière, Alice
Duchemin, Louis
César, Roberto Marcondes
Mary, Arnaud
Vinga, Susana
Sagot, Marie-France
Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title_full Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title_fullStr Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title_full_unstemmed Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title_short Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
title_sort totoro: identifying active reactions during the transient state for metabolic perturbations
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905348/
https://www.ncbi.nlm.nih.gov/pubmed/35281848
http://dx.doi.org/10.3389/fgene.2022.815476
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