The interest to develop the biofuels, such as bioethanol, has been increasing in the last years. The most common process to produce bioethanol is the first generation technology, in which the most used organism is the yeast Saccharomyces cerevisiae. However, the high concentration of ethanol induces cell toxicity in this yeast, constituting the main limiting factor to produce this fuel. Despite of lots of effort to understand this phenomenon have been applied, the ethanol tolerance is poorly understood from systemic perspective. Thus, the aim of this project is to identify the systemic signatures associated to ethanol tolerance using bioinformatics tools, transcriptome analysis, artificial intelligence and modeling of dynamic networks. The preliminary results of this study showed that the network features represent feasible biological networks. Moreover, it is the first time that clustering of edge degrees for all generated networks reports that degree properties are closely related to the ethanol tolerance in yeast (the clusters obey the ethanol percentages). Thus, we expect that this proposal allow fundamental knowledge increasing concerning this phenomena; moreover the result may be used for technological application, allowing to rank the best genes/process candidates to genetic engineering focused to increase the ethanol tolerance. This project is part of a Regular Project FAPESP (process number 2015/12093-9) managed by professor Guilherme Targino Valente; preliminary results of this project are also included here. Additional documents to evaluate this proposal are also inserted in the end of this document.
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