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Integrated multi-omics analysis and metabolic network simulations applied to Saccharomyces cerevisiae for second generation ethanol production

Grant number: 19/12914-3
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): January 01, 2020
Effective date (End): December 31, 2020
Field of knowledge:Biological Sciences - Genetics
Principal Investigator:Guido Costa Souza de Araújo
Grantee:Lucas Miguel de Carvalho
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID

Abstract

Currently, ethanol is the most consumed biofuel in the world and Brazil is a highlight in production. One of the great advances in the production of ethanol is the production from lignocellulosic biomass that is called second-generation ethanol (2G ethanol) or cellulosic ethanol and is considered the largest industrial biotechnology product in the world. Second-generation technology is one of the most promising in the development stage on the planet, and it consists of transforming the polymers of the plant cell wall into ethanol. In order to produce a broad understanding of cellular functioning in the conditions of interest, bioinformatics, in association with the various ómics, has a fundamental role, so as to enable a complete study of cellular metabolism (DNA, mRNA, proteins and metabolites) indicating bottlenecks and also directing to new genetic modifications, in order to maximize the metabolic flow. The tooling needed to analyze and predict the metabolites that will be formed encompasses the use of Machine Learning algorithms (ML), using features related to production, chemical, network and experimental data (such as RNA-Seq and proteomics). Along with Petri Net simulation data from metabolic networks and Flux Balance Analysis (FBA) data. Therefore, this project aims to integrate bioinformatics analysis methodologies, which include metabolic simulations and ML algorithms, to elucidate the metabolic bottlenecks present in the production of 2G ethanol. (AU)