Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biotechnology

Background:

• (13)C metabolic flux analysis (MFA) is crucial for quantitative metabolic network analysis.
• Classical stationary MFA has limitations in experimental duration and accounting for non-steady-state conditions.

Purpose of the Study:

• To extend computational methods for stationary (13)C MFA to the isotopically instationary regime.
• To develop and apply computational tools for instationary carbon labeling experiments (CLEs).
• To improve the accuracy and statistical properties of metabolic flux estimations.

Main Methods:

• Implementation of high-performance computing for instationary (13)C MFA.
• Development of tools for simulation, sensitivity analysis, parameter fitting, and identifiability analysis.
• Application to the central metabolism of Escherichia coli.

Main Results:

• Demonstrated the strengths of instationary MFA over stationary MFA, particularly in statistical properties.
• Showcased improved quality of flux estimates and the ability to make statements about pool sizes.
• Introduced a novel ranking method for optimal experimental design of sampling times.

Conclusions:

• Instationary (13)C MFA offers significant advantages for metabolic network analysis, including reduced experimental time and enhanced flux estimation.
• The developed computational tools provide a comprehensive framework for instationary MFA.
• While not all fluxes may be fully identifiable, the instationary approach substantially improves quantitative insights into metabolic processes.