David Heckmann

The paper develops a deep learning model to predict Michaelis constants for enzyme-substrate combinations using structural features.

The paper uses matrix factorization to uncover consistent regulatory signals across diverse gene expression datasets, demonstrating its utility in multi-omics analysis.

The paper estimates in vivo enzyme turnover numbers (kcats) in E. coli using metabolic specialist strains and shows their stability and predictive power over in vitro kcats.

The paper models resource allocation in C3 and C4 plants to show that C4 plants have limited phenotypic plasticity, suggesting their resource allocation reflects ancestral environments rather than current conditions.

The paper develops a quantitative framework to predict ROS stress responses in E. coli across molecular, network, and genetic levels.