Verena Siewers

This paper introduces ExpressionGAN, a deep learning model that generates functional regulatory DNA with specified expression levels, outperforming random mutagenesis approaches.

The paper upgrades the GECKO method to create enzyme-constrained models for yeast and human, revealing common metabolic adaptations under stress.

This study improves (2S)-naringenin production in yeast by fine-tuning p-coumaric acid synthesis, reducing its extracellular accumulation and increasing yield to 933 mg/L.

This study shows that a single chromosome yeast strain has reduced ethanol metabolism and growth compared to wildtype.

The paper evaluates the potential of Yarrowia lipolytica's alkane-sensing TFs as a biosensor in Saccharomyces cerevisiae but finds they do not function as expected in the new host.

The paper uses deep learning to show that gene expression levels are encoded in both coding and non-coding regions of the gene regulatory structure across multiple organisms.

The paper identifies stress-induced genes in budding yeasts and finds they are enriched for young, non-essential genes that evolve rapidly.

The paper shows that the transcription start site landscape in Saccharomyces cerevisiae remains stable across different environmental conditions and identifies a negative correlation between TSS shape index and expression levels.

This study investigates the regulatory impact of acetylation sites on Fas2p in yeast by mutating lysine residues to glutamine or arginine and analyzing effects on fatty acid biosynthesis.

The paper evaluates the reverse β-oxidation pathway in Saccharomyces cerevisiae for fatty acid production and finds no significant improvement in efficiency.