A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Biotechnology

Background:

• Dynamic flux balance analysis (DFBA) is crucial for modeling bioprocess dynamics.
• DFBA relies on maximizing biological objectives under kinetic constraints.
• Previous methods often require ad hoc selection of objectives and constraints.

Purpose of the Study:

• To develop a systematic, data-based approach for identifying biological objectives in DFBA.
• To determine the minimal set of active constraints for accurate model predictions.
• To overcome limitations of previous subjective choices in DFBA model construction.

Main Methods:

• A novel algorithm is proposed to infer objective functions and active constraints from data.
• The method incorporates experimental error into the model fitting process.
• It systematically identifies necessary constraints for matching model outputs to experimental data.

Main Results:

• The approach was validated using in silico data from an Escherichia coli model.
• The method successfully analyzed experimental data from Bordetella Pertussis batch fermentation.
• It demonstrated the ability to find objective functions and constraints without arbitrary choices.

Conclusions:

• The proposed data-based DFBA method offers a systematic way to build accurate bioprocess models.
• This approach reduces the need for subjective decisions in selecting objectives and constraints.
• It enhances the reliability and predictive power of DFBA for bioprocesses.