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.