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Integrative analysis applied to ethanol tolerance in Saccharomyces cerevisiae strains: an approach using transcriptomes, proteomes, system biology and machine learning

Abstract

Nowadays the world demands to the reduction of fossil fuels is increasing and consequently the ethanol as a fuel has been shown as an excellent alternative source of energy. The yeast Saccharomyces cerevisiae is the microorganism most used to the ethanol production due to its high fermentative capacity and high resistance of stressors from this process. However, the ethanol concentration is one of the most limiting factors for the production of this fuel because the concentration increasing of this compound disturbs the cells and reduces the productivity. Usually the researches concerning the ethanol tolerance are prone to the biological description of this process or to the production/selection of more tolerant strains; on the other hand, the molecular and systemic aspects of this process are still poorly understood. Thus, the present project aim to generate and analyze the transcriptomes and proteomes of strains with different ethanol tolerances submitted to an experiment of this tolerance; the functional-comparative analysis will be the focus of this project to identify the systemic aspects relative to this phenotype. For this purpose, the differential expression analysis, protein-protein interactions, regulatory and metabolic networks will be retrieved, modeled, compared and the patterns for those systems will be extracted as well. The use of several techniques, methods, analysis, model constructions, pattern extraction and the further conclusions based on the expected results concerning the ethanol tolerance in S. cerevisiae phenomenon (which never was investigated under a systemic-integrative context as here proposed), will be the most important innovations brought with this project. (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)
ALMEIDA, RODRIGO DE OLIVEIRA; VALENTE, GUILHERME TARGINO. Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning. PLANT GENOME, v. 13, n. 3 AUG 2020. Web of Science Citations: 0.
DE ALMEIDA, LAUANA FOGACA; DE MORAES, LEONARDO NAZARIO; DOS SANTOS, LUCILENE DELAZARI; VALENTE, GUILHERME TARGINO. Development and comparative analysis of yeast protein extraction protocols for mass spectrometry. Analytical Biochemistry, v. 567, p. 90-95, FEB 15 2019. Web of Science Citations: 1.

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