{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Galvao Ferrarini M"],"pubmed_abstract":["<b>Motivation:</b> 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. <b>Results:</b> 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 <i>Escherichia coli</i> and we were able to predi"],"journal":["Frontiers in genetics"],"pagination":["815476"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8905348"],"repository":["biostudies-literature"],"pubmed_title":["Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations."],"pmcid":["PMC8905348"],"pubmed_authors":["Julien-Laferriere A","Cesar RM","Ziska I","Andrade R","Sagot MF","Vinga S","Galvao Ferrarini M","Mary A","Duchemin L"],"additional_accession":[]},"is_claimable":false,"name":"Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.","description":"<b>Motivation:</b> 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. <b>Results:</b> 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 <i>Escherichia coli</i> and we were able to predi","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T07:44:08.475Z","creation":"2025-04-04T07:44:08.475Z"},"accession":"S-EPMC8905348","cross_references":{"pubmed":["35281848"],"doi":["10.3389/fgene.2022.815476"]}}