Tobias von der Haar

This paper introduces an AI framework to improve mRNA therapeutic manufacturing by predicting and optimizing sequence design for in vitro transcription efficiency.

The paper develops a computational model to study codon decoding times, showing that near-cognate tRNAs significantly influence these times and validating the model against experimental data.

This paper develops engineered yeast biofilms that deliver PET-degrading enzymes directly to substrates, demonstrating a proof-of-principle for biofilm-based bioremediation without enzyme purification.

The paper investigates how ribosome speed affects translational control in yeast, finding that 20% of genes show queueing ribosomes indicative of elongation control, with distinct regulatory responses to stress.

The paper develops a mathematical model to quantify codon usage bias across fungal and bacterial genomes, revealing taxonomic variation and implications for model organisms.

The paper investigates codon usage-dependent selective pressure on high copy-number genes in yeast, finding that optimal codon usage is beneficial under high expression but selected against otherwise.

The paper develops an MS-based assay to detect amino acid misincorporation in gene expression and shows codon usage affects error rates in E. coli but not in S. cerevisiae.