Mark Kathol

The paper uses machine learning on proteomics data to predict growth rates of Rhodopseudomonas palustris on lignin-derived substrates and identifies proteomic features that encode growth potential across environments.

This study investigates lignin degradation mechanisms in Rhodopseudomonas palustris using multi-omics and CRISPRi to identify essential pathways and improve sustainable valorization of lignin.

This paper uses artificial neural networks to predict growth rates of Rhodopseudomonas palustris on lignin breakdown products by analyzing transcriptomics and proteomics data, identifying key transport proteins as critical predictors.

This study examines Rhodopseudomonas palustris' ability to remove perfluorooctanoic acid (PFOA) and its toxicological response, finding temporary incorporation of PFOA into the cell membrane and diauxic growth patterns at varying concentrations.