The overall aim of our research is to use computer-based techniques to understand the metabolism and energy-production of the mitochondrion, and identity the genetic causes of genetic disease. Our first objective has been the development of MitoMiner, a public resource database that describes the proteins of the mitochondrion from many different species, including human. We gather and integrate results from experimental studies that identify mitochondrial proteins, and link them with data about diseases, metabolism and evolution. Using this database, researchers can find evidence about whether a protein is mitochondrial, which tissues it is expressed in, which species it is found in, and if it is associated with disease. Next, we are using MitoMiner with its catalogue of mitochondrial proteins to build computer models of mitochondrial metabolism and energy production. We are building tissue-specific models, as mitochondrial metabolism varies amongst different organs and in different diseases, such as cancer. We use “flux balance analysis” to simulate the flow of metabolites in these models, and predict what happens to energy production and metabolism when mitochondrial proteins are affected by genetic mutations as in mitochondrial diseases, or the supply of metabolites is increased or decreased, as in heart disease or cancer. In collaboration with experimental researchers and clinicians, we are investigating how modelling can lead to a greater understanding of mitochondrial function in health and disease, leading to possible new therapies and drug targets. Taken together, mitochondrial genetic diseases affect about 1 in 8,000 live births, and may be the cause of many pregnancy losses. For affected families, genetic diagnosis is essential for counselling, can enable appropriate clinical management, and eventually drive suitable treatment of patients. A new and important diagnostic strategy for identifying disease genes is the exome sequencing of patients with mitochondrial disease. Furthermore, the identified variants in novel mitochondrial genes provide valuable insight into mitochondrial function and disease, can be transferred to diagnostic tests, including high-throughput targeted sequencing, and potentially suggest therapies to alleviate symptoms. The MRC MBU carries out whole exome sequencing of patient samples in-house. But exome sequencing produces huge amounts of data and identifies candidate variants in 1000’s of genes, meaning computational analyses are essential to manage and mine these data. Thus we have built an analysis pipeline and are developing and applying computational tools to analyse these data and identify and prioritise variants in mitochondrial genes that can be pathogenic.