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

在正确的时间将正确的药物带给正确的患者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，详细的知识获取和表示是必不可少的。先前捕捉药物基因疾病相互作用的努力已经在大规模组学倡议中实现，例如Lincs连接图项目(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，不能捕获患者特定的模式。个体的基因图谱是疾病和患者异质性的关键决定因素，而这些网络结构中缺少这种异质性。在这个项目中，您将发展在多个尺度上模拟多组学数据集的专业知识。您的重点将是将来自英国生物库或英国基因组学等来源的遗传和多组学存储库数据引入到我们现有的系统中，这些系统代表ExScience a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人工智能的方法，您将创建与疾病相关的过程模型。您将评估它们在对患者分组进行分层以及为定向治疗干预确定最合适的拓扑点方面的有效性。您将与ExScience a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，你将在达米安·斯梅德利教授的团队中工作，他们在100,000基因组计划(https://www.nejm.org/doi/full/10.1056/NEJMoa2035790).中研究疾病的遗传原因方面拥有专业知识作为君主倡议(MonarchInitiative.org)的一部分，他们使用本体论对个体患者中的基因-表型关联进行建模，参考人类疾病，以及模型生物体，以阐明没有先前人类数据的基因(IMPC；MUSEPANOTYPE.org)，您将利用这一点来改进网络中的知识表示。

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.