Hierarchical modelling of microbial communities

Bioinformatics. 2023 Jan 1;39(1):btad040. doi: 10.1093/bioinformatics/btad040.


Summary: The human body harbours a plethora of microbes that play a fundamental role in the well-being of the host. Still, the contribution of many microorganisms to human health remains undiscovered. To understand the composition of their communities, the accurate genome-scale metabolic network models of participating microorganisms are integrated to construct a community that mimics the normal bacterial flora of humans. So far, tools for modelling the communities have transformed the community into various optimization problems and model compositions. Therefore, any knockout or modification of each submodel (each species) necessitates the up-to-date creation of the community to incorporate rebuildings. To solve this complexity, we refer to the context of SBML in a hierarchical model composition, wherein each species's genome-scale metabolic model is imported as a submodel in another model. Hence, the community is a model composed of submodels defined in separate files. We combine all these files upon parsing to a so-called 'flattened' model, i.e., a comprehensive and valid SBML file of the entire community that COBRApy can parse for further processing. The hierarchical model facilitates the analysis of the whole community irrespective of any changes in the individual submodels.

Availability and implementation: The module is freely available at https://github.com/manuelgloeckler/ncmw.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacteria
  • Genome
  • Humans
  • Metabolic Networks and Pathways
  • Microbiota*
  • Software*