Hameri Tuure

This paper introduces a constraint-based framework, Network Response Analysis (NRA), to improve metabolic engineering by incorporating physiological constraints and genome editing limitations.

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.

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.