etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

Area of Science:

• Microbiology and Synthetic Biology
• Metabolic Engineering
• Computational Biology

Background:

• Bacillus subtilis is a key microorganism for bioproduction, but existing genome-scale metabolic models (GEMs) have limitations.
• Previous GEMs for B. subtilis suffer from low predictive power and lack regulatory information, hindering their application in metabolic engineering.

Purpose of the Study:

• To develop an improved GEM for B. subtilis (iBsu1209) and a comprehensive multiscale model (etiBsu1209).
• To enhance the predictive accuracy of metabolic models for B. subtilis.
• To utilize the multiscale model for guiding metabolic engineering strategies to overproduce valuable compounds.

Main Methods:

• Construction of iBsu1209, a GEM with 1209 genes, 1595 metabolites, and 1948 reactions.
• Application of machine learning to fill metabolic network gaps and improve prediction accuracy (89.3%).
• Development of Model Tool software for multiconstraint model reconstruction and analysis, leading to the multiscale model etiBsu1209 integrating enzymatic, thermodynamic, and regulatory constraints.

Main Results:

• The new GEM, iBsu1209, demonstrated high accuracy in predicting mutant growth under various conditions.
• The multiscale model etiBsu1209 was successfully constructed, integrating multiple layers of biological constraints.
• Metabolic engineering guided by etiBsu1209 resulted in a 2.2-fold increase in menaquinone-7 production (153.94 mg/L).

Conclusions:

• etiBsu1209 represents the first comprehensive multiscale model for Bacillus subtilis.
• This advanced model provides a robust platform for the rational computational design of B. subtilis cell factories.
• The developed models and tools facilitate improved bioproduction of nutraceuticals and other valuable compounds.