Paul van Lent

This study uses in-silico DBTL cycles to evaluate how process parameters affect strain optimization outcomes in metabolic engineering.

This paper applies a machine learning-assisted recommendation strategy with exploration-exploitation balance to optimize p-coumaric acid production in yeast, achieving a 137% increase over parent strains.

The paper introduces a framework for training metabolic kinetic models using neural ODEs, demonstrating efficient parameterization on SBML models and glycolysis data.

This study analyzes antibiotic resistance patterns in soil metagenomes across different types and locations, identifying key resistance genes and their environmental influences.

The paper benchmarks long-read de novo assemblers for eukaryotic genomes, finding that Flye performs best overall, and longer reads can improve assembly quality depending on genome complexity.