Joep P. J. Schmitz

Machine Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: a p-Coumaric Acid Case Study

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.

Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models

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

Glycolysis revisited: from steady state growth to glucose pulses

The paper develops a physiologically informed kinetic model of yeast glycolysis connected to central metabolism, showing it captures experimental dynamics and explains pathway contributions under glucose perturbations.