Wendel Batista da Silveira

The paper develops a toolbox to model promiscuous enzyme activities in metabolic models, showing that underground metabolism enhances metabolic flexibility and robustness.

The paper uses multi-omics integration to identify mechanisms of ethanol stress response and respiro-fermentative metabolism in Kluyveromyces marxianus.

CAMEL predicts protein abundances by combining constraint-based modeling and machine learning, achieving high accuracy in E. coli and moderate performance in S. cerevisiae.

This paper presents lista-GEM, the first genome-scale metabolic model of Lipomyces starkeyi, identifies metabolic engineering targets to improve lipid production from various carbon sources.

This study predicts protein abundance using machine learning based on codon usage bias, achieving high accuracy and validating predictions in metabolic models.