Genome-scale metabolic network models: from first-generation to next-generation

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Thousands of genome-scale metabolic network models (GSMMs) have been developed over the past two decades.
• Early GSMMs faced prediction accuracy limitations due to a lack of constraints.
• Next-generation GSMMs integrate omics data, constraints, and diverse biological models for enhanced functionality.

Purpose of the Study:

• To review the evolution of GSMMs from first-generation to next-generation models.
• To discuss the major applications of GSMMs in industrial biotechnology and human health.
• To highlight challenges and future trends in GSMM development, including machine learning integration.

Main Methods:

• Literature review of GSMMs published since 1999.
• Analysis of advancements in GSMM construction and constraint integration.
• Summary of GSMM applications in phenotype prediction, metabolic engineering, biomarker discovery, and drug targeting.

Main Results:

• GSMMs have advanced from basic network representations to sophisticated predictive tools.
• Next-generation GSMMs demonstrate improved accuracy and broader applicability through data integration.
• Key applications include guiding metabolic engineering in industrial biotechnology and understanding disease mechanisms in human health.

Conclusions:

• GSMMs are powerful tools for systems biology, with continuous advancements enhancing their predictive capabilities.
• The integration of machine learning presents a promising future direction for GSMM development and application.
• Future GSMMs are expected to offer deeper insights into complex biological systems and facilitate novel biotechnological and therapeutic strategies.