Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic models (GEMs) are crucial for metabolic engineering but have limitations in capturing biological complexity due to their single gene-protein-reaction data structure.
• Existing GEMs struggle to accurately predict cellular phenotype from genotype because they do not fully represent biological intricacies.

Purpose of the Study:

• To review recent advancements in the development of multiscale Genome-scale metabolic models (GEMs).
• To highlight the integration of omics data and machine learning in constructing more comprehensive metabolic models.
• To discuss the frameworks, toolkits, algorithms, challenges, and future perspectives in multiscale GEM development.

Main Methods:

• Summarizing recent literature on multiscale GEM development, including multiconstraint, multiomic, and whole-cell models.
• Reviewing the application of machine learning techniques in the construction and refinement of GEMs.
• Analyzing frameworks, toolkits, and algorithms used for building advanced metabolic models.

Main Results:

• Multiscale models, incorporating multiple constraints or omics data, offer improved predictions compared to traditional GEMs.
• Machine learning approaches are increasingly utilized to enhance GEM construction and data integration.
• Various frameworks and toolkits are available for developing sophisticated multiscale metabolic models.

Conclusions:

• Multiscale GEMs represent a significant advancement over traditional models, enabling a more accurate understanding of cellular metabolism.
• The integration of omics data and machine learning is key to unlocking the full potential of multiscale metabolic modeling.
• Further development in multiscale GEMs is essential for addressing complex challenges in metabolic engineering and systems biology.