Georgios Fengos

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

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

The paper systematically reduces a genome-scale metabolic model of E. coli to study how model complexity affects metabolic sensitivity coefficients and shows consistent metabolic engineering conclusions across different model sizes.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

The paper analyzes how uncertainty in intracellular fluxes and concentrations affects kinetic models of E. coli metabolism and proposes a workflow to improve prediction reliability.