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Constraint-based metabolic control analysis for rational strain engineering.

Sophia Tsouka¹, Meric Ataman¹, Tuure Hameri¹

¹Laboratory of Computational Systems Biology (LCSB), EPFL, CH-1015, Lausanne, Switzerland.

Metabolic Engineering

|April 25, 2021

Summary

This summary is machine-generated.

Network Response Analysis (NRA) offers a sophisticated framework for rational metabolic engineering, integrating multiple constraints for improved genetic strain design. This method enhances traditional approaches by incorporating physiological data for more effective metabolic engineering strategies.

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Area of Science:

Metabolic Engineering
Systems Biology
Synthetic Biology

Background:

Metabolic Control Analysis (MCA) is a key method for metabolic engineering but lacks physiological constraints.
Genome editing advancements necessitate improved rational design strategies for cellular metabolism.

Purpose of the Study:

To introduce Network Response Analysis (NRA), a constraint-based framework for rational genetic strain design.
To integrate Metabolic Control Analysis (MCA), Thermodynamically-based Flux Analysis (TFA), and biological constraints into a unified platform.
To identify optimal metabolic engineering targets considering cellular limitations and genome editing restrictions.

Main Methods:

Network Response Analysis (NRA) formulated as a Mixed-Integer Linear Programming problem.
Integration of MCA, TFA, and biologically relevant constraints, including genome editing restrictions.
Demonstration of NRA's equivalence to Flux Balance Analysis (FBA) and TFA for broad applicability.

Main Results:

NRA provides a comprehensive platform for identifying metabolic engineering targets.
The framework accommodates various optimization criteria and physiological constraints.
NRA enhances classical MCA by incorporating parameterized biological constraints.
Multiple alternative optimal strategies can be generated based on user-defined objectives and boundaries.

Conclusions:

NRA is a powerful alternative to MCA for rational metabolic engineering.
The framework effectively incorporates physiological data at flux, concentration, and enzyme expression levels.
NRA facilitates advanced genetic strain design by considering complex biological limitations.