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

在正确的时间为正确的患者提供正确的药物是制药药物研究中最大的全球挑战之一。为了真正了解疾病的机制及其目标机会，详细的知识捕获和表示至关重要。以前捕获药物基因疾病相互作用的努力已经在大规模组学计划中实现，例如LINCS连接MAP项目（http：//www.ilincs.org/help/signatureLibraries/Connectivity-Map-signatures）和基因组规模知识图（https：//www.annualreviews.org/doi/10.1146/annurev-genom-120219-080406）。然而，这些努力产生了高质量的参考生化网络，其不捕获患者特异性模态。个体的遗传特征是这些网络结构中缺失的疾病和患者异质性的关键决定因素。在这个项目中，您将开发在多个尺度上对多组学数据集进行建模的专业知识。您的重点将是将来自英国生物银行或英国基因组学等来源的遗传和多组学存储库数据引入我们现有的系统中，这些系统代表了Exscientia感兴趣的与年龄相关的疾病的关键领域。使用最先进的系统生物学和基于AI的方法，您将创建疾病相关过程的模型。您将评估它们在对患者组进行分层以及确定最适合靶向治疗干预的拓扑部位方面的效用。您将与Exscientia的目标分析和发现团队密切合作，并从他们在人工智能和发现平台技术方面的培训和专业知识中受益匪浅。在QMUL，你将在教授达米安·斯梅德利的团队谁在调查疾病的遗传原因在100，000基因组计划的专业知识工作（https：//www.nejm.org/doi/full/10.1056/NEJMoa2035790）。作为Monarch Initiative（monarchinitiative.org）的一部分，他们使用本体来模拟个体患者的基因-表型关联，参考人类疾病，以及模型生物体，以揭示没有先前人类数据的基因（IMPC; mousephenotype.org），您将利用这一点来改善网络中的知识表示。

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.