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Enumerating multidimensional metabolic stories: models and methods

Grant number: 15/13430-9
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): September 01, 2016
Effective date (End): January 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Roberto Marcondes Cesar Junior
Grantee:Ricardo Luiz de Andrade Abrantes
Home 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. 514-523, JAN 15 2020. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.