Multi-omics systems biology modelling of patient networks in age-related diseases

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Bringing the right drug to the right patient at the right time is one of the biggest global challenges in pharmaceutical drug research. To truly understand the mechanics of disease and its target opportunities, detailed knowledge capture and representation is essential. Previous efforts to capture drug gene disease interactions have been realised in large scale omics initiatives such as the LINCS Connectivity MAP project (http://www.ilincs.org/help/signatureLibraries/Connectivity-Map-signatures) and genome scale knowledge graphs (https://www.annualreviews.org/doi/10.1146/annurev-genom-120219-080406). However, these efforts produce high quality reference biochemical networks that do not capture patient specific modalities. The genetic profile of an individual is a key determinant of disease and patient heterogeneity that is missing from these network structures. In this project you develop expertise in modelling multi-omics datasets at multiple scales. Your focus will be to introduce genetic and multi-omics repository data from sources such as UK Biobank or Genomics England into our existing systems representing key areas of age-related disease that are of interest to Exscientia. Using state of the art systems biology and AI-based methods, you will create models of disease-relevant processes. You will evaluate their utility in stratifying patient groups, and in identifying the most appropriate topological sites for targeted therapeutic intervention. You will work closely with the Target Analysis and Discovery teams at Exscientia and benefit significantly from their training and expertise in both AI and discovery platform technologies. Within QMUL, you will work within Professor Damian Smedley's team who have expertise in investigating the genetic cause of disease in the 100,000 Genomes Project (https://www.nejm.org/doi/full/10.1056/NEJMoa2035790). As part of the Monarch Initiative (monarchinitiative.org) they use ontologies to model gene-phenotype associations in individual patients, reference human diseases, as well as model organisms to shed light on genes with no prior human data (IMPC; mousephenotype.org), and you will leverage this to improve knowledge representation in the networks.