Daniel Weilandt

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

This study shows that fever during influenza A virus infection increases antiviral signaling by promoting aberrant RNA synthesis and innate immune activation.

This study develops a multi-nutrient differential equation model to show how competitive catabolism between glucose, fatty acids, and ketones under fasting conditions leads to systemic metabolic homeostasis, with implications for obesity-induced diabetes.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

The paper uses kinetic models to guide metabolic engineering of S. cerevisiae strains, resulting in improved p-coumaric acid production with minimal impact on growth rate.

This paper introduces a framework for rational strain design in E. coli that minimizes phenotype perturbation using mixed-integer linear programming and kinetic models to improve anthranilate production.

The paper introduces SKiMpy, a Python package for efficiently building and analyzing large-scale kinetic models in biological systems.

This paper introduces REKINDLE, a deep learning framework that generates kinetic models of metabolic networks with dynamic properties, demonstrating its efficiency and versatility through three studies.

The paper shows mitochondrial respiration is more proteome-efficient than aerobic glycolysis in yeast and mammals, with flexibility arising from maintaining a fermentation-capable proteome.

The paper develops a framework to analyze optimal enzyme operation modes under varying conditions, showing that enzyme efficiency depends on reactant concentrations and mechanism type.