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Towards dynamic genome-scale models.

David Gilbert, Monika Heiner, Yasoda Jayaweera

Briefings in Bioinformatics

|October 18, 2017

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Analyzing large metabolic models is challenging. This study introduces a new workflow using efficient simulations and model checking to analyze dynamic behavior in genome-scale metabolic models (GEMs).

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.

Keywords:

approximative stochastic simulation clustering data analytics delta leaping formal analysis model checking model-based design reaction profiling scalability subsystems behaviour whole-genome-scale metabolic models

Related Experiment Videos

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.