Microbiome data analysis and modeling

Intraspecific genomic variability within microbiomes (‘micro-diversity’) is still poorly understood. Yet, it is intricately linked to ecological processes in microbial systems, including fluxes of energy and nutrients. WP6 aims to determine microbial micro-diversity in natural samples through the development of population dynamic models and bioinformatic resources.

In addition, WP6 aims to advance metabolic modeling of microbiomes by using metabolic networks models and allowing the exchange of metabolites between members of the community. Finally, the spatial component of microbiomes will be addressed through biophysical models that explain and predict the spatial arrangement of cells over time.

The objectives of WP6 are to:

  • Build databases, bioinformatics and computational resources required for the analysis of metagenomics and metatranscriptomics data
  • Develop mathematical models that describe metabolic networks and spatial arrangements of microbes within host-microbiome systems
  • Integrate knowledge gained on the metabolic networks and biophysical interactions for the organisms in WP1-5.

WP6 focuses on the development of databases, mathematical models and computational methods for the analysis and the design of microbiomes. The use of meta-omics readouts to analyze variation in microbial communities at population-level resolution will contribute to a better understanding of the genetic (rather than taxonomic) diversity in natural microbiomes.

Work Package Leader
Prof. Vassily Hatzimanikatis

Latest publications

Metabolic reconstitution of germ-free mice by a gnotobiotic microbiota varies over the circadian cycle
Hoces, D., Lan, J., Sun, W., Geiser, T., Stäubli, M., Barazzone, E., Arnoldini, M., Challa, T.D., Klug, M., Kellenberger, A., Nowok, S., Faccin, E., Macpherson, A.J., Stecher, B., Sunagawa, S., Zenobi, R., Hardt, W.-D., Wolfrum, C., Slack, E. (2022).
Computational tools and resources for designing new pathways to small molecules
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).
ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).