AUTHOR=Galvão Ferrarini Mariana , Ziska Irene , Andrade Ricardo , Julien-Laferrière Alice , Duchemin Louis , César Roberto Marcondes , Mary Arnaud , Vinga Susana , Sagot Marie-France 

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

JOURNAL=Frontiers in Genetics

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.815476

DOI=10.3389/fgene.2022.815476

ISSN=1664-8021

ABSTRACT=<p><bold>Motivation:</bold> 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.</p><p><bold>Results:</bold> In this paper, we present <sc>Totoro</sc> 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 <sc>Totoro</sc> to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from <italic>Escherichia coli</italic> 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 <italic>E. coli</italic> core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks.</p><p><bold>Availability:</bold><sc>Totoro</sc> is an open source method (available at <ext-link ext-link-type="uri" xlink:href="https://gitlab.inria.fr/erable/totoro" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.inria.fr/erable/totoro</ext-link>) 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.</p>