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Systems biology and synthetic biology to re-engineering metabolic pathways in Saccharomyces cerevisiae: dealing with ethanol tolerance


Bioethanol has been shown as an excellent alternative energy source of fossil fuels. The yeast Saccharomyces cerevisiae is the main microorganism used for bioethanol production. However, the ethanol concentration is one of the limiting factors for ethanol production because at high concentrations, this compound disturbs cells and reduces the productivity. Despite many studies on this topic, the systemic aspects of this process are still poorly understood and the selection of target genes to improve the ethanol tolerance for genetic engineering is not trivial. FAPESP has granted a project (FAPESP 2015/12093-9 with Guilherme Valente as PI) focusing on the use of OMICs, cell biology and systems biology to study yeast ethanol tolerance. Preliminary results show that network features are related to the ethanol tolerance and that neither growth rate nor cell viability are main attributes to increase this tolerance. Thus, it indicates that reductionist approaches may be not the best for genetic engineering to improve this phenotype. The goal of this proposal is to identify the sub-systems responsible for ethanol tolerance and to provide a technological implementation using the systems biology information. Herein, we will interference with the function of specific gene sets using the CRISPR-Cas9 technology for gene editing. The work will provide a deeper insight in the processes that determine ethanol tolerance, and this knowledgebase will be used to develop new strains with improved ethanol production efficiency as well as ethanol tolerance. Moreover, it will also contribute to the professional qualifications of the Brazilian students actively participating of an international collaboration. Students will be training in the use of the CRISPR-Cas9 technology and there will be knowledge transfers from Netherlands to Brazil and vice versa. Overall, the program will result in advanced technological development. (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.

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