BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme...

Area of Science:

• Biotechnology and metabolic engineering
• Computational biology and systems biology

Background:

• Chemical bioproduction is crucial for a decarbonized society.
• Predicting metabolic flux changes from enzyme perturbations is vital for computational design.
• Existing structural sensitivity analysis (SSA) methods may lack sufficient network information for unambiguous predictions.

Purpose of the Study:

• To develop a novel method, BayesianSSA, that integrates environmental information into SSA predictions.
• To address limitations of traditional SSA by incorporating empirical data.
• To improve the accuracy of predicting system responses to enzyme perturbations in bioproduction.

Main Methods:

• Developed BayesianSSA, a Bayesian statistical model building upon SSA.
• Extracted environmental information from perturbation datasets.
• Integrated this environmental information into SSA predictions.

Main Results:

• Successfully applied BayesianSSA to synthetic and real datasets of Escherichia coli's central metabolic pathway.
• Demonstrated that BayesianSSA effectively integrates environmental information into SSA predictions.
• Showed that BayesianSSA's posterior distribution aligns with known pathways enhancing succinate export flux.

Conclusions:

• BayesianSSA enhances the prediction of metabolic flux changes in bioproduction.
• This advancement is expected to accelerate chemical bioproduction processes.
• BayesianSSA contributes to the progress of metabolic engineering and synthetic biology.