Precise metabolic modeling in post-omics era: accomplishments and perspectives

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Computational Biology

Background:

• Microbes are vital for sustainable bio-product synthesis, but intracellular metabolism knowledge gaps limit applications.
• Genome-scale metabolic models (GEMs) offer a framework for understanding cellular metabolism and guiding genetic modifications.
• Traditional GEMs lack comprehensive biological data (e.g., enzyme kinetics, thermodynamics), limiting predictive power in complex environments.

Purpose of the Study:

• To provide an overview of innovations in GEMs, focusing on multi-constrained modeling and machine learning integration.
• To highlight advancements in analytical approaches for enhanced metabolic modeling.
• To discuss the application of these advanced models in microbial strain development and biomolecule production.

Main Methods:

• Review of current research on genome-scale metabolic modeling.
• Exploration of multi-constrained modeling incorporating omics data.
• Integration of machine learning algorithms with metabolic models.

Main Results:

• GEMs are evolving beyond stoichiometric analysis to incorporate multi-omics data and biological constraints.
• Machine learning significantly improves the predictive accuracy and mechanistic understanding of GEMs.
• Advanced GEMs offer enhanced capabilities for rational genetic engineering and strain optimization.

Conclusions:

• Integrating multi-omics data and machine learning into GEMs is essential for overcoming limitations of traditional models.
• These advanced modeling strategies hold significant promise for accelerating the development of microbial cell factories for bio-molecule production.
• Further research in this area will drive innovation in genetic refinement, strain development, and yield enhancement.