Rationale To date, there are few effective treatments for patients with mitochondrial disease and those available are predominantly supportive in nature with no proven treatment efficacy, and poor understanding of the links between the gut microbiota, mitochondrial disease, GI dysmotility and patient health and quality of life. Treatments include various antibiotics and laxatives which are generic and not disease specific. Long term effects of drugs are unknown and the impact these have on the GI tract and gut microbiota in mitochondrial disorders are currently unknown. It is essential to optimise supportive therapeutic strategies and design novel modalities to improve clinical management. Although advances in technology now provide more biological information than ever before, the complexity and volume of data generated exceeds the ability to analyse, interpret and translate this information back into the clinical management, highlighting the need to increase clinical analytical capabilities. The use of bioinformatics and computational biology to combine metagenomics data relating to the gut microbiota and metadata (patient characteristics; phenotype/genotype) is one approach to identify and predict what factors, such as drugs, phenotype and genotype, induce gut microbiota dysbiosis.