A new multidimensional systems biology analysis to predict xylose-fermenting yeast strain differences under 2G ethanol fermentation
The fight against global warming has become more necessary due to the large emission of greenhouse gasses, and biofuels emerge as the main combatant to this scenario. In Brazil, 2G ethanol is a promising new strategy for increasing the production of biofuels, using xylose-fermenting yeasts, since xylose is the main source of sugar in this strategy. Due to problems related to the metabolic bottleneck, redox unbalance and development of efficient strains, deeper studies of Systems Biology turn out to be necessary and supported by omics integration methods. In this study, we propose a new multidimensional systems biology analysis, which integrates strategies such as network analysis, differential gene expression analysis, co-expression analysis, machine learning methods, constraint-based modeling, and evolutionary algorithms from public transcriptome data to identify in silico differences in the metabolism between two xylose-fermenting yeast strains (XI and XR/XDH). This approach is based on executing consistent strategies independently and integrating their results to increase the elucidation of a given problem. As a result, we identify differences and similarities in their metabolism and show how the use of a multidimensional approach adds value to in silico bioinformatics analyses. Finally, by integrating all of our results, we generated a list of possible common target genes for strains XI and XR/XDH, while also noting differences in their metabolism, mainly regarding access to mitochondria and production of cofactors. We emphasize that the identified genes can be promoted to genetic engineering studies, and that each independent solution can further elucidate the joint role between the needs of the yeast to supply the oxidative stress environment of 2G ethanol fermentations.
