Microbiome Systems Biology
The development of next-generation sequencing that enables obtaining thousands to millions of reads per run at affordable costs for the scientific community, has revolutionized the field of medical microbiology. By assessing much deeper layers of microbial communities researchers were able to explore in detail both “who’s there?” and “what are they doing?” and develop models that describe the interplay of hosts, commensal microbes and diseases. Since tools and statistical methodologies are becoming faster and more specialized for complex microbial communities we expect that soon metagenomics will allow a full characterization of the community that will subsequently shift the focus from descriptive to mechanistic modeling of the host-microbiome interactome. The primary goals of our group are to: (i) create spatially and temporally resolved maps of the microbial world of the human, built and natural environment, (ii) develop a roadmap for discovering how microbes travel between different parts in the body and between various environments we come into contact every day, and (iii) harness this knowledge to explain the rise of diseases in urbanized parts of the world.
Microbial ecology has witnessed tremendous progress over the last decade empowered by new sequencing technologies. These innovations in DNA sequencing resulted to a paradigm shift in our understanding of pathogenic processes and gave birth to a new term, namely “pathobiome”. In the pathobiome concept the microbial community, in which the pathogenic agent exists, has a clear impact on the persistence, transmission and evolution of pathogens. It is therefore vitally important to apply concepts and approaches of systems biology for identifying species that contribute, either as partners or antagonists, to pathogen’s survival, extinction or dispersal.
In order to truly reveal the microbial community interactions that lead to onset of pathogenesis we should go beyond the identification of statistical associations and emphasize on the development of computational models that describe the spatio-temporal dynamics of the pathogen and its biotic environment. The current studies based on single –omics or insufficient combination/association of metagenomic, metatranscriptomic and meta-metabolomic data limit the deep understanding and in-depth investigation of the biotic and abiotic factors that may disturb the pathobiome. Clearly, developing multi-meta-omic systems biology frameworks would allow us to investigate previously ignored aspects, like growth dynamics and activity of the microbes and construct systems-level predictive models for pathobiome research.
To demonstrate the power of our approach, we apply systems level thinking, analysis and modelling to investigate the contribution of the biotic environment on the growth and dissemination of Candida albicans.