THYME: Machine learning and metabolic modelling of key bacteria growing in biofilm mode in the human gut

Project Description

The commensal bacteria of the human intestine have been found to significantly affect human health. They produce Vitamins B and K, important amino acids and essential enzymes for carbohydrate fermentation that help improving digestion. In addition, they have been linked with a variety of serious human diseases such as obesity, diabetes, autoimmune disorders, allergies and even mental health. This has led to the development of a wide range of probiotic products that include certain alive microorganisms, such as Lactobacillus, Bifidobacterium and Saccharomyces. In addition, gut bacteria often form biofilms, robust structures that harbour mixed bacterial communities, attached to the intestinal epithelium. The ability of such species to form biofilms is directly linked to their ability to colonise the gut, which is essential for ensuring good efficiency of a probiotic product. This project will provide a methodological advance in the field of biofilm metabolic modelling by implementing multi-species modelling within a machine learning method. In the proposed case study, it will also provide insights into how machine learning can be applied to find the most optimal conditions for specific microbial communities in the human gut. The project combines computer science, mathematics and biotechnology leading to a long-term relationship.