Completed Cells, Biochemistry & Physiology Public Health & Healthcare

MICA: UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO).

In plain English

AI plain-English summary

A consortium of seven UK research centres is building a dedicated computing infrastructure to store, integrate, and analyse vast amounts of medical data—from genetic sequences to blood chemistry—all linked to individual patient health records. This matters because most medical data today sits in silos: a patient’s genome is stored separately from their metabolic profile, and neither is easily combined with their clinical history. Without a unified platform, researchers cannot spot the patterns that link a specific genetic variant, a particular metabolic fingerprint, and a disease outcome. The gap is not in collecting data but in connecting it. If the infrastructure succeeds, it will enable “stratified medicine”—matching treatments to a patient’s precise biology rather than their broad diagnosis. For example, two people with type 2 diabetes might receive different drugs because their metabolic profiles predict different disease trajectories. The platform will also train a new generation of biomedical researchers comfortable working across computing, engineering, and medicine. The project is not fundamental science; it is an explicit effort to build the digital backbone that makes large-scale, data-driven medical discovery possible.

View original technical description
This proposal brings together a group of leading multidisciplinary teams in medical, chemical, metabolic, statistical and computational sciences from across Imperial College London (ICL, lead institution) and its partners. These include the Institute of Cancer Research, the European Molecular Biology Laboratory-European Bioinformatics Institute, the Universities of Oxford, Swansea and Nottingham, and the MRC Clinical Sciences Centre and MRC Human Nutrition Research Centre, supported by strong partnerships and collaborative links with industry, the NHS and the National Institute for Health Research funded Biomedical Research Centres and Units. The proposal seeks support to build a DEDICATED INFRASTRUCTURE in data storage, aggregation, analysis and visualisation of diverse types of biomedical data. These come from standard clinical sources through to different types of information including from genetic analysis, and metabolic information, that help us to define patients or people studied in the general population using a holistic "systems medicine" framework. The GLOBAL AIM is to make major advances in understanding the causes and reasons for disease progression of common human diseases such as cancer, cardiovascular disease, respiratory disease and metabolic disorders such as type 2 diabetes and obesity. In this way we aim to create new disease diagnostics and prognostics aimed at the individual patient ("stratified medicine") through innovations in medical bioinformatics with powerful computing capability. The programme will create unprecedented capacity to i) DEVELOP powerful new approaches for computation and analysis of large-scale, complex, multi-source medical data; ii) INTEGRATE information linking multiple different types of biological information from analysis of blood and urine samples (e.g., metabolic, genomic, analysis of the microbial genome) to different diseases, disease progression and outcomes; hence to iii) help UNDERSTAND the causes and mechanisms of disease and improve individual disease classification for better patient treatments and safety; also to iv) TRANSFORM the training of the biomedical researchers of the future through creation of a seamless interdisciplinary environment spanning biomedicine, physical sciences, computing and engineering. In particular we will capitalise on computational expertise that has led to the development of a partnership in medical information infrastructure and service between universities and the pharmaceutical industry called eTRIKS, and related software platforms such as tranSMART, an "open source" solution for managing data and research knowledge in clinical studies. We also have world-leading expertise in metabolic "fingerprinting" and systems medicine approaches manifested in the MRC-NIHR National Phenome Centre located at ICL, and underpinned by excellence in genomics, computational sciences and advanced data modelling and visualisation. This project has a very broad collaboration with industrial sectors including major pharmaceutical companies, instrument vendors, IT and informatics companies. Our project covers the complete healthcare envelope of data generating activity and analysis from the level of basic measurement sciences through to the understanding of gene-environment interactions and disease mechanisms to the creation of knowledge systems for better clinical decision making based on detailed knowledge of individual patient biology. This application is strengthened by the decision of ICL to establish a major interdisciplinary centre for 'big data' at the new Imperial West campus, ensuring sustainability over the longer term. The project aims to deliver top class science, a robust informatics platform and in-depth scientific data and knowledge to contribute to the state of the art of UK and international medical research.

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Researchers

Alan A Ashworth (Co-Investigator)Brian Thomson (Co-Investigator)Christoph Steinbeck (Co-Investigator)Claire O'Donovan (Co-Investigator)Enrico Petretto (Co-Investigator)Jeremy Nicholson (Co-Investigator)Julian Griffin (Co-Investigator)Paul Elliott (Principal Investigator)Robert Glen (Co-Investigator)Ronan Lyons (Co-Investigator)Susanna Sansone (Co-Investigator)Yi-Ke Guo (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

MICA: Medical Bioinformatics: Data-Driven Discovery for Personalised Medicine
Innovative Technologies for Stratified and Experimental Medicine
CLIMB-BIG-DATA: A Cloud Infrastructure for Big-Data Microbial Bioinformatics
Infrastructure for collaboration: Leeds MRC Medical Bioinformatics Centre
The MRC Consortium for Medical Microbial Bioinformatics

Original classification

Research Grant

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