Reconstruction and Computational Modelling for Inherited Metabolic Diseases

遗传性代谢疾病的重建和计算模型

• 批准号: 10083717
• 金额: $ 64.2万
• 依托单位: NEWCASTLE UNIVERSITY
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10083717
• 关键词: Reconstruction; Computational; Modelling; Inherited; Metabolic

Our overall objectives are to accelerate the diagnosis, and enable personalised management, of inherited metabolic diseases (IMDs). Established academic technology for statistical genomic analysis, deep learning-based prediction of protein structure, and wholebody metabolic network modelling shall be applied to generate personalised computational models, given patient-derived genomic, transcriptomic, proteomic and metabolomic data. To train diagnostic models, a comprehensive clinical team will recruit 1,945 diagnosed patients with a wide variety of IMDs, then validate the clinical utility of personalised computational models on a set of 685 undiagnosed patients. An enhanced human metabolic network reconstruction, especially for lipid metabolism, reaction kinetics and inherited metabolic disease pathways, will increase the predictive capacity of cellular and whole-body metabolic network models. As an exemplar for other IMDs, personalised computational modelling will be used to identify compensatory and aggravating mechanisms that associate with clinical severity in Gaucher disease. The predictive capacity of personalised models will be validated by comparison with additional empirical investigations of protein structure and function as well as metabolomics, tracer-based metabolomics and proteomics of patientderived in vitro disease models. To maximise the potential for impact, personalised modelling software will be developed to be generally applicable to a broad variety of IMDs, and implemented in a way that is both accessible to clinicians and admissible to regulatory authorities. Sustainability will be promoted by development of a roadmap for a European foundation to aid personalised diagnosis and management of IMDs, informed by broad stakeholder consultation. This is a unique opportunity to realise the potential of personalised computational modelling for a broad set of rare diseases, which is a field where European collaboration is an essential for progress

我们的总体目标是加快遗传性代谢性疾病(IMD)的诊断，并实现个性化管理。已建立的用于统计基因组分析、基于深度学习的蛋白质结构预测和全身代谢网络建模的学术技术应应用于生成个性化计算模型，给出患者衍生的基因组、转录组、蛋白质组和代谢组学数据。为了训练诊断模型，一个全面的临床团队将招募1945名患有各种IMD的确诊患者，然后在一组685名未诊断的患者身上验证个性化计算模型的临床实用性。加强人体代谢网络的重建，特别是对脂质代谢、反应动力学和遗传性代谢性疾病途径的重建，将提高细胞和全身代谢网络模型的预测能力。作为其他IMD的范例，个性化计算模型将用于确定与高谢病临床严重程度相关的代偿和加重机制。个性化模型的预测能力将通过与对蛋白质结构和功能的额外经验研究以及患者体外疾病模型的代谢组学、基于示踪剂的代谢组学和蛋白质组学的比较来验证。为了最大限度地发挥影响的潜力，将开发个性化的建模软件，使其普遍适用于各种类型的IMD，并以临床医生可访问和监管当局可接受的方式实施。可持续发展将通过制定一个欧洲基金会的路线图来促进，以帮助在广泛的利益相关者咨询的基础上对IMD进行个性化诊断和管理。这是一个独特的机会，可以实现对一系列罕见疾病进行个性化计算建模的潜力，这是一个欧洲合作对进步至关重要的领域