Technological advances have allowed scientists to gather large amounts of data about a vast array of species, organisms and single cells. Ìì·¢ÓéÀÖÆåÅÆ_Ìì·¢ÓéÀÖAPP-¹ÙÍø|ÏÂÔØ researchers are using mathematical modelling, machine learning and other algorithms to extract information and patterns from large data sets to further our understanding of disease.
Contemporary scientific research benefits from rapid technological developments that enable the characterisation and quantification of biological molecules at unprecedented scale. Scientists can generate vast data that provide insight into the complex interplay of molecules within organisms. Interrogation and interpretation of these data inform the structure, function and interaction of molecules over time.
We use ¡®Omic technologies comprehensively to evaluate DNA (genomics), RNA (transcriptomics) and proteins (proteomics). We study small molecules using metabolomics. Microorganisms are investigated in a targeted manner using microbiomics or more broadly to characterise mixed samples using metagenomics.
At the University of Ìì·¢ÓéÀÖÆåÅÆ_Ìì·¢ÓéÀÖAPP-¹ÙÍø|ÏÂÔØ, we generate vast datasets using these approaches across a wide range of environments and species. We work closely with NHS partners to use these capabilities to understand human disease and inform its clinical management. We bring together medical and biological scientists with mathematicians, computer and data scientists to develop and apply methods that exploit these data to their fullest potential.
From analysing patient genomes, to carrying out metagenomic analysis of water samples to using mass spectrometry metabolic profiling techniques, our scientists are studying the unique processes that take place within cells that can lead to disease or poor health outcomes in humans and help track changes in the environment.
We are using data to answer clinical questions in areas such as cancer, autoimmune and respiratory diseases with the help of clinical colleagues we are translating our findings into novel techniques for clinicians to treat their patients, make predictions about prognosis and drug responsiveness.
Ìì·¢ÓéÀÖÆåÅÆ_Ìì·¢ÓéÀÖAPP-¹ÙÍø|ÏÂÔØ researchers collaborate with partners at:
We are at a very exciting time in Life Science Research. The potential for novel discovery using ¡®omics technologies combined with the computer science methodologies is immense.
