Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling

Area of Science:

• Biotechnology
• Metabolic Engineering
• Synthetic Biology

Background:

• Optimizing microbial metabolism is crucial for cost-effective bioproduct and biofuel production.
• Tuning enzyme expression for desired pathway flux requires integrating multiomics data with biological knowledge.

Purpose of the Study:

• To identify key enzymes in *Yarrowia lipolytica* that correlate with itaconate production.
• To apply Bayesian metabolic control analysis to quantify uncertainties in flux control coefficients (FCCs).

Main Methods:

• Integrated a metabolic model with multiomics measurements in *Yarrowia lipolytica*.
• Employed a design-build-test-learn strategy.
• Quantified uncertainty in flux control coefficients (FCCs) and correlated enzymes with boundary flux.

Main Results:

• Identified five significant FCCs and five correlated enzymes impacting itaconate production.
• Highlighted phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) as key enzymes.
• Demonstrated these enzymes are in rate-limiting reactions for enhanced itaconic acid production.

Conclusions:

• Phosphoglycerate mutase, ACSm, HCO3E, PPAm, and HSDxi are critical targets for improving itaconate biosynthesis.
• Metabolic control analysis integrated with multiomics provides a robust framework for optimizing microbial cell factories.