Flux sampling and context-specific genome-scale metabolic models for biotechnological applications

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Genome-scale metabolic models (GEMs) are widely applied in metabolic engineering, drug discovery, and microbiome design.
• Current applications often focus on predicting optimal states, but some areas require exploring the full feasible flux space.
• Integrating transcriptomic or proteomic data to predict tissue-specific or disease-specific fluxes is a growing need.

Purpose of the Study:

• To review and analyze methods for sampling feasible metabolic flux distributions from GEMs.
• To identify limitations and challenges associated with existing approaches.
• To provide guidelines for overcoming shortcomings and highlight areas for future methodological development.

Main Methods:

• Revisiting and evaluating diverse computational methods for flux sampling in metabolic models.
• Analyzing the applicability of these methods for predicting biologically relevant states and integrating omics data.
• Identifying conceptual and practical barriers in current flux analysis techniques.

Main Results:

• Existing methods for flux sampling have limitations that can hinder accurate predictions for complex biological systems.
• The integration of omics data (transcriptomics, proteomics) presents significant challenges for flux prediction.
• Current approaches may not fully capture the dynamic and context-specific nature of cellular metabolism.

Conclusions:

• New methodologies are required to address the limitations in current flux sampling and prediction techniques.
• Overcoming conceptual barriers is essential for advancing the application of GEMs in areas like personalized medicine and synthetic ecology.
• Further development is needed to enable comprehensive exploration of metabolic states and improve data integration.