Human gut microbiome, big data and inflammatory bowel disease

Abstract:

Gut microbiota is unique for each individual and it is characterized by diversity, ability of adaptation to the environmental influences as well as nutrition. The metabolism of gut microbiota is complementary to the metabolism of the human being, building together the metabolism of the common organism of the higher degree. The symbiotic and mutualistic interaction of human organism and „self“ microbiota represents the concept of the complex „superorganism“ – the holobiont as an evolutionary unit. The gut disbiosis as the disorder of the microbiomal diversity, richness and variety of species is linked to gastrointestinal, metabolic and autoimmune diseases. The big data, derived from epidemiological, clinical and data sets from molecular biology, are analyzed by methods of systems biology and machine learning (artificial intelligence). Meta „omics“ represents a research of microbial communities from different perspectives: what is the potential of production of certain microbial community (metagenomics); actual process of production by expression of genes (metatranscriptomics) and what has been produced by certain microbial community (metaproteomics, metabolomics). In the context of personalized and precision medicine approach, a knowledge about the microbioma and notions of patterns of the disease biomarkers in regard to prediction of the disease course and therapeutic outcome, derived by the use of of systems biology and machine learning on big data, would greatly improve caring for patients with inflammatory bowel disease.

Key words:
artificial intelligence; big data; human gut microbioma; inflammatory bowel disease; machine learning; systems biology