Alexander Bockmayr

The paper introduces a method to decompose flux vectors into elementary flux modes using geometric insights, focusing on low-degree decompositions in genome-scale metabolic networks.

This paper analyzes the geometric properties of elementary flux modes in metabolic networks, showing that EFMs in the relative interior of the flux cone are rare and introducing a complexity measure based on their degree.

This paper introduces time-optimal adaptation (TOA), a constraint-based modeling approach that explains cellular metabolic responses to environmental changes and demonstrates its ability to explain experimental phenomena.

This paper introduces r-deFBA, a framework that integrates metabolic dynamics, resource allocation, and transcriptional regulation for predicting cellular behavior over time.

The paper introduces MEMos, minimum sets of elementary flux modes, to efficiently represent all steady-state behaviors of metabolic networks without enumerating all EFMs.

The paper introduces a metabolic-regulatory network model formalized as a hybrid automaton to integrate metabolism with transcriptional regulation and enzyme dynamics.