Enumerating multidimensional metabolic stories: models and methods

• Grant number: 15/13430-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2016
• End date: January 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Roberto Marcondes Cesar Junior
• Grantee: Ricardo Luiz de Andrade Abrantes
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences, AP.TEM
• Associated scholarship(s): 17/05986-2 - Metabolic stories from transcriptomic data, BE.EP.PD

Abstract

In recent years, the development of new sequencing, spectrometry and spectroscopy techniques led to a massive amount of data available for the analysis of biological phenomena. However the data acquisition techniques are generating much more data than the scientists can deal with. These data are usually complex, imprecise and incomplete in the sense that they cover only some target subsystems. Inferring something from the data may become a very complex task. Moreover, many of the available methods suppose that the input data are complete and exact, which hinders their real applicability.Considering the growing availability of omics data, it was proposed by Acuna et. al [Acuna2012] a method that allows a better understanding of metabolomic data. Such method can be used to understand how organisms react to stress situations or even how a symbiotic relation was stablished between organisms. The method organises metabolomic data into the metabolic stories. A story corresponds to a possible flux of matter between measured compounds that can explain their variations. In this project we propose to extend the method proposed in [Acuna2012] considering the stoichiometry of reactions. We want also to integrate fluxomic and proteomic data with the metabolomic data in the model. In this way, the new proposed problem is the problem if identifying metabolic stories which correctly explain the data from metabolomic, fluxomic and proteomic experiments. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PUSA, TANELI; FERRARINI, MARIANA GALVAO; ANDRADE, RICARDO; MARY, ARNAUD; MARCHETTI-SPACCAMELA, ALBERTO; STOUGIE, LEEN; SAGOT, MARIE-FRANCE. MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network. Bioinformatics, v. 36, n. 2, p. 10-pg., 2020-01-15. (17/05986-2, 15/13430-9)

PUSA, TANELI; FERRARINI, MARIANA GALVAO; ANDRADE, RICARDO; MARY, ARNAUD; MARCHETTI-SPACCAMELA, ALBERTO; STOUGIE, LEEN; SAGOT, MARIE-FRANCE. MOOMIN - Mathematical explOration of `Omics data on a MetabolIc Network. Bioinformatics, v. 36, n. 2, p. 514-523, JAN 15 2020. (15/13430-9, 17/05986-2)

GALVAO FERRARINI, MARIANA; ZISKA, IRENE; ANDRADE, RICARDO; JULIEN-LAFERRIERE, ALICE; DUCHEMIN, LOUIS; CESAR, ROBERTO MARCONDES; MARY, ARNAUD; VINGA, SUSANA; SAGOT, MARIE-FRANCE. Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. FRONTIERS IN GENETICS, v. 13, p. 12-pg., 2022-02-21. (15/22308-2, 15/13430-9)