Mariana Galvão Ferrarini1,2, Irene Ziska1,3, Ricardo Andrade1,4, Alice Julien-Laferrière5, Louis Duchemin1, Roberto Marcondes César4, Arnaud Mary1,3, Susana Vinga6, Marie-France Sagot1,3. 1. Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France. 2. Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France. 3. INRIA Grenoble Rhône-Alpes, Villeurbanne, France. 4. Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil. 5. Soladis GmBH, Basel, Switzerland. 6. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
Abstract
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.
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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