Philipp Wendering

C4TUNE predicts photosynthesis kinetic parameters from response curves, enabling efficient simulation and identification of limiting factors in maize genotypes.

KineticGP improves prediction of leaf photosynthesis traits in maize by integrating genomic data with kinetic models of C4 photosynthesis.

The paper develops a temperature-dependent enzyme-constrained metabolic model for Arabidopsis thaliana to predict growth responses and identify genes affecting thermal growth.

CAMEL predicts protein abundances by combining constraint-based modeling and machine learning, achieving high accuracy in E. coli and moderate performance in S. cerevisiae.

The paper shows that integrating data improves turnover number estimates and metabolic predictions for E. coli and S. cerevisiae.

The paper constructs an enzyme-constrained genome-scale metabolic model of Rhizophagus irregularis to predict its metabolic capabilities and physiological responses to environmental cues.

This paper introduces a model reduction approach for biochemical networks that preserves steady-state fluxes and reduces metabolite counts by up to 99% in Escherichia coli models.

The paper introduces COMMIT, a method that improves microbial community models by considering metabolite leakage and community composition, leading to better gap filling and identification of microbial roles.