Metabolic stories from transcriptomic data

• Grant number: 17/05986-2
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: June 27, 2017
• End date: June 26, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Roberto Marcondes Cesar Junior
• Grantee: Ricardo Luiz de Andrade Abrantes
• Supervisor: Marie-France Sagot
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Institut National de Recherche en Informatique et en Automatique (INRIA Rhône-Alpes), France
• Associated to the scholarship: 15/13430-9 - Enumerating multidimensional metabolic stories: models and methods, BP.PD

Abstract

This is a research project to apply for an internship abroad, to be held at INRIA, in Lyon, France.The work consists of formally redefining the concept of metabolic stories by considering transcriptomic and metabolomic data together and developing a method that is able to enumerate metabolic stories from these two types of data.A metabolic story is originally defined considering metabolomic data, that is, measures of the variations of certain compounds produced by an organism between two different experimental situations.Essentially, a metabolic story is a set of reactions by which we can have a flow of matter that explains the variation of the measured compounds.We wish to redefine this concept in the context of transcriptomic data, that is, data that quantify the presence of messenger RNA molecules that are expressed at a given time in a cell or a cell population.We are interested in studying, from a theoretical point of view, the complexity of enumerating stories according to this new definition. From a practical point of view, we also wish to develop a computational method that is sufficiently robust to enumerate such structures and to develop a pipeline for the analysis of generated histories. This pipeline will be based on transforming the generated data into characteristic vectors and grouping them through clustering methods in order to simplify their visualization and analysis. (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. (15/13430-9, 17/05986-2)

ANDRADE, RICARDO; DOOSTMOHAMMADI, MAHDI; SANTOS, JOAO L.; SAGOT, MARIE-FRANCE; MIRA, NUNO P.; VINGA, SUSANA. MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering. BMC Bioinformatics, v. 21, n. 1, FEB 24 2020. (17/05986-2)

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)