📄 Published in Metabolic Engineering

ET-OptME is a computational framework that integrates enzyme constraints and thermodynamic feasibility into metabolic engineering design. It incorporates two core algorithms:

• ET-EComp: Predicts up- or down-regulation targets by comparing enzyme abundance ranges between reference and overproduction states.
• ET-ESEOF: Scans enzyme responses to increasing product flux to identify monotonic trends for regulation.

This approach improves prediction accuracy and precision by minimizing enzyme costs and avoiding thermodynamic bottlenecks. ET-OptME targets whole enzymes or enzyme complexes, overcoming the limitations of classical reaction-centric models.

• Combines stoichiometric, enzyme, and thermodynamic constraints
• Protein-centric target prediction
• Built-in case studies for Corynebacterium glutamicum
• Supports COBRApy-compatible metabolic models

• Tested with Gurobi 9.5 and CPLEX 12.10
• Optimization backends required for LP/MILP solving

git clone https://github.com/Jingyi-Cai/ET-OptME.git
cd ET-OptME
# Set up your Python environment (conda recommended)
pip install -r requirements.txt