Microbial Data Science
To create an interdisciplinary systems biology approach where Environmental Microbiology, Modeling, Microbial Physiology, Bioinformatics and Engineering meet to create out-of-the-box thinking towards prediction and control of microbial interactions in complex microbial communities.
Environmental health regulates the conditions, under which 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. To diminish the boundaries between natural sciences and engineering, We will work to define environmental health by exploring microbial interactions. We want to achieve this mission by developing concepts and theories to expand modeling of microbial communities to the vast diversity found in natural ecosystems with focus on prediction and control of ecosystem processes during disturbance events.
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 Data Science group are based on genetic potential of microbial communities, integration of multi-omics analysis and predictive biology. Currently these topics cover:
We are shedding light into the factors influencing the recovery of genomes from metagenomes towards the development of neutral and niche theories to study microbial interactions using multi-omics data.• Expanding limits of detection of traditional Omics techniques: In collaboration with the Flow Cytometry group in our department, we are using a combination of fluorescence-activated cell sorting and metagenomics sequence to increase the limits of detection of metagenome assembled genomes in microbial communities.
Use of '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.
• Species in the era of metagenome assembled genomes: We are developing techniques to exploit the diversity of metagenome assembled genomes by resolving species (and potentially strain) variation in complex microbial communities.
• Exploration of regulatory network in complex microbial communities: We are developing deep learning approaches to map transcription factors (and potentially their binding sites) in complex microbial communities.
• High-throughput genomic analysis: with the exponential increase of genomic (and multi-omics) data available in public repositories, we are creating user-friendly pipelines that can be used by microbiologists and microbial ecologists to analyze hundreds or thousands of genomes simultaneously.
We are developing tools to adapt predictive analytics to relevant questions and hypothesis relevant to microbiologists, environmental microbiologists and microbial ecologists.
• Prediction of health and dysbiosis states in microbial communities: We are developing pipelines to detect health and dysbiosis from multi-omics bioindicators using machine learning.
• Prediction of ecosystem services based on omics data: We are developing pipeline mixing traditional omics and machine learning to determine ecosystem services in a multi-omics perspective. We are currently using benzoate degradation and carbon fixation pathways as a model pathways.• Prediction of trends in microbiology: We are developing machine learning approaches to understand trends in microbiology research from 1990 to 2020 and to predict the future trends for this field in the next 7 years.
Dr. Stefanía Magnúsdóttir
Marcos Vinicios Fleming Bicalho
Guest ScientistsPolonca Štefanič (Assistant Professor, Slovenia)
Alexander Bartholomäaus (Postdoc, Germany)
Lummy Monteiro (Postdoc, USA)
Sandra Cristina Godinho Pires da Silva (PhD student, Portugal)
Merve Nida Basturk (B.Sc. student, Turkey)
Fatma Chafra (B.Sc. student, Turkey)
Anderson Paulo Avila Santos
- Interdisciplinary approach to understand microbial interactions in highly complex system.
- Genomics of microbial species using genomes metagenome-assembled genomes.
- Metabolic modeling of microbial systems where links between genes, reactions and metabolites are available.
- Development of concepts and theories to scale modeling of the molecular mechanisms of inter-species interactions to the diversity found in natural ecosystems.
- Open discussion with team members and collaborators to set goals and deadlines as well as to analyze progress of the work
- Each member of the team is responsible for a different set of skills and for connecting them to the different partners within team members of collaborators
- Out of the box thinking is stimulated
- Establishment of collaborations within UFZ, the Helmholtz family, German and international universities and research groups.