The Microbial Systems Data Science group envision an interdisciplinary Systems Biology approach where Microbial Ecology, Modeling, Microbial Physiology and Bioinformatics meet to create out of the box thinking towards prediction microbial interactions in complex systems.
Environmental health regulates the conditions life is sustained on Earth. A clear definition of environmental health is necessary to allow the implementation of practices that guarantee sustainable exploration of our limited natural resources. The Microbial Systems Data Science group works to define environmental health by exploring microbial interactions. We want to achieve our mission by developing concepts and theories to expand modeling of microbial communities to the vast diversity found in natural ecosystems with focus on the fate of chemicals in the environment.
Our group strives to assess environmental health of terrestrials and man made environments by predicting how resilient/stable a microbial community is to specific disturbances. A special emphasis is put on the development of concepts and theories to scale microbial interactions to the real diversity found in nature.
The key research topics of the Microbial Systems Data Science group are based on genetic potential of microbial communities and integration of multi-omics analysis. Currently these topics cover:
- Use of (in silico) mock microbial communities to test microbial ecology theories: (i) Elucidation of factors influencing the recovery of genomes from metagenomes, (ii) Development of neutral and niche theories to study microbial interactions using multi-omics data.
- From microbial 'Big Data' to novel ecological concepts and theories: Use of public available 'omics' databases to elucidate long-time assumptions in microbiology.
- Scaling modeling of microbial interactions to the diversity found in natural ecosystems: Definition of concepts and theories to use partial genome-scale metabolic modeling as a tool to explore microbial community interactions.