Hierarchical modelling of microbial communities.

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• The human microbiome plays a crucial role in health, but the functions of many microbes are unknown.
• Accurate genome-scale metabolic network models are essential for understanding microbial community composition and function.
• Existing modeling tools face challenges in managing complex community structures and modifications.

Purpose of the Study:

• To develop a novel hierarchical modeling framework for constructing and analyzing human microbial communities.
• To overcome the complexity of updating community models upon individual species modifications.
• To facilitate the integration and analysis of genome-scale metabolic network models of multiple microorganisms.

Main Methods:

• Utilized the Systems Biology Markup Language (SBML) for hierarchical model composition.
• Imported individual species' genome-scale metabolic models as submodels within a larger community model.
• Developed a 'flattening' process to combine separate submodels into a single, comprehensive SBML file parsable by COBRApy.

Main Results:

• Successfully implemented a hierarchical modeling approach for microbial communities.
• The 'flattened' model allows for comprehensive analysis of the entire community.
• The framework facilitates analysis regardless of individual submodel changes, simplifying updates and modifications.

Conclusions:

• The hierarchical modeling approach simplifies the analysis of complex microbial communities.
• This method enhances the ability to study the human microbiome and discover microbial contributions to health.
• The developed module provides a flexible and efficient tool for microbial community modeling.