Towards dynamic genome-scale models

Area of Science:

• Systems Biology
• Computational Biology
• Metabolic Engineering

Background:

• Genome-scale metabolic models (GEMs) are complex, containing thousands of reactions and metabolites.
• Simulating the dynamic behavior of large GEMs presents significant computational challenges.
• Sophisticated computational tools are essential for analyzing GEMs under various growth conditions.

Purpose of the Study:

• To provide a methodology and workflow for analyzing the dynamic behavior of large-scale metabolic models.
• To aid modelers in selecting and applying appropriate tools for dynamic analysis of GEMs.
• To enable abstract views of GEM behavior for better understanding.

Main Methods:

• Developed a methodology using publicly available tools for profiling and analyzing whole-genome-scale biochemical models.
• Employed an efficient approximative stochastic simulation method to address dynamic simulation challenges in GEMs.
• Applied simulative model checking with temporal logic, clustering, and data analysis on time-series data of reaction rates and metabolite concentrations.

Main Results:

• Successfully profiled and analyzed whole-genome-scale metabolic models.
• Overcame dynamic simulation problems in large GEMs using an efficient stochastic simulation approach.
• Extended analysis to track the evolution of reaction-oriented properties within subnets over time, identifying dead and functional subsystems.

Conclusions:

• The proposed methodology and workflow facilitate the dynamic analysis of large-scale metabolic models.
• Abstract views of GEM behavior are generated, making complex models more interpretable.
• Demonstrated the methodology on a reduced model of Escherichia coli K-12 metabolism, showing its applicability in metabolic engineering and synthetic biology.