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Wendel Batista da Silveira

Publications and source records attributed to Wendel Batista da Silveira.

2 recordsLinked to original sources

Genomic features, metabolism, and biotechnological applications of Candida tropicalis and other non-albicans Candida species.

The production of bio-based products by yeasts from agroindustrial byproducts is a key strategy for advancing circular bioeconomy. While Saccharomyces species remain the predominant industrial yeasts, their limited ability to assimilate lactose, pentoses, and glycerol, as well as their sensitivity to lignocellulose-derived inhibitors, restricts their efficient application in bioprocesses based on using industrial byproducts as fermentation media. In contrast, several non-albicans Candida species exhibit broad substrate utilization capacities and enhanced tolerance to industrial stresses, making them attractive candidates for the bioconversion of agroindustrial residues. This review critically examines recent advances in the genomic, metabolic, and physiological characterization of promising non-albicans Candida species, including Candida tropicalis, Candida parapsilosis, Candida viswanathii, Candida sojae, and Candida maltosa. Emphasis is given to genome-scale metabolic models, carbon assimilation pathways, stress-response mechanisms, and metabolic engineering approaches aiming at the production of value-added compounds. By identifying current achievements, knowledge gaps, and biotechnological bottlenecks, this review highlights the potential of these yeasts as emerging platforms for sustainable bioprocesses within a circular bioeconomy framework.

Biotechnology

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning