Form Leads to Function: Multi-scale modelling of Lymph Node Architecture in Response to Vaccine Adjuvants

形式导致功能：响应疫苗佐剂的淋巴结结构的多尺度建模

• 批准号: 2270506
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2270506
• 关键词: Form; Leads; Function; Multi; scale

Although vaccines have been in use for over 250 years, the mechanisms driving protective immunity are not fully understood. The continual emergence of potentially lethal human and domestic animal pathogens, and the need to generate protective vaccines against chronic bacterial and viral infections requires a better understanding of mechanisms of action to develop the next generation of vaccines. Vaccine immune responses involve a complex interplay between the highly diverse immune repertoire of T and B cells, stromal fibroblasts and innate immune cells in tissue draining lymph nodes (LN). We have shown during the immune response, LNs rapid remodel in a process dependent on the type of vaccine adjuvant, regulating the type, duration and specificity of the immune response. We and others have developed multi-scale agent-based models of the lymph node to study immune cell dynamics during vaccine responses and formation of LN microenvironments. In this project we aim to use datasets including imaging, cytometry and gene expression from different adjuvants to design, develop and validate computational models. We have developed tool kits to both visualise model outputs and statistically analyse parameter space in multi-scale computational models. Through combining neural network emulation with genetic evolutionary algorithms to perform multi-objective optimisation, we aim to identify optimal LN architectures in silico that stimulates protective immune responses permitting better vaccine design.Benefits and project impact: Developing a robust evidence-based model of the vaccine mediated immune responses has an essential role in understanding how normal animal and human lymph node microenvironments stimulate optimal immune responses to provide long term protection. This has the potential to permit the development of more effective vaccines for infectious organisms and cancer.Skills Training: The student will have the opportunity to learn key interdisciplinary skills in mathematics, computer programming, data analysis and in molecular and cellular immunology. The student will be jointly based in the Kennedy Institute of Rheumatology and the Mathematical Institute with academic and translational skills training opportunities. Interdisciplinary seminars at the Kennedy and Jenner Institutes host world leading seminars in immunology and vaccine development, the Wolfson Centre for Mathematical Biology in mathematical biology. Simomics is an SME based in York and has previously developed Virtual Disease Laboratories to develop therapeutics for Leishmaniasis (LeishSim) and is working on models to study immune responses. Through this collaboration the student will be able to utilise tools developed by Simomics including virtual laboratories, model development syntax languages and statistical tool kits to assist in the generation and confidence in the lymph node model. Development of a robust evidence-based lymph node model will provide a platform to simulate how to generate optimal vaccine responses that have a critical role in healthy living and aging.Industrial Secondment: The secondment will take place at Simomics offices which based at the York Science Park in York. York is a highly vibrant academic city that provides a beautiful environment. The student will be based in a group of software developers who have extensive experience in software development and computational modelling.

虽然疫苗已经使用了250多年，但驱动保护性免疫的机制尚未完全了解。潜在致命的人类和家畜病原体的不断出现，以及产生针对慢性细菌和病毒感染的保护性疫苗的需要，要求更好地理解作用机制以开发下一代疫苗。疫苗免疫应答涉及T和B细胞、基质成纤维细胞和组织引流淋巴结（LN）中的先天免疫细胞的高度多样性免疫库之间的复杂相互作用。我们已经表明，在免疫应答期间，LN在依赖于疫苗佐剂的类型的过程中快速重塑，调节免疫应答的类型、持续时间和特异性。我们和其他人已经开发了多尺度的基于代理的淋巴结模型，以研究疫苗反应和LN微环境形成过程中的免疫细胞动力学。在这个项目中，我们的目标是使用数据集，包括成像，细胞计数和不同佐剂的基因表达来设计，开发和验证计算模型。我们已经开发了工具包来可视化模型输出和统计分析多尺度计算模型中的参数空间。通过将神经网络仿真与遗传进化算法相结合来执行多目标优化，我们的目标是通过计算机识别最佳LN架构，从而刺激保护性免疫反应，从而实现更好的疫苗设计。找到有力的证据-基于疫苗介导的免疫应答的模型在理解正常动物和人类淋巴结微环境如何刺激最佳免疫应答方面具有重要作用。提供长期保护。技能培训：学生将有机会学习数学，计算机编程，数据分析以及分子和细胞免疫学等关键的跨学科技能。该学生将在肯尼迪流变学研究所和数学研究所共同工作，提供学术和翻译技能培训机会。肯尼迪和詹纳研究所的跨学科研讨会主办了世界领先的免疫学和疫苗开发研讨会，沃尔夫森数学生物学中心的数学生物学。Simomics是一家位于约克的中小企业，此前曾开发过虚拟疾病实验室来开发利什曼病（LeishSim）的治疗方法，目前正在研究免疫反应的模型。通过这种合作，学生将能够利用Simomics开发的工具，包括虚拟实验室，模型开发语法语言和统计工具包，以帮助生成和信任淋巴结模型。一个强大的循证淋巴结模型的发展将提供一个平台，以模拟如何产生最佳的疫苗反应，在健康的生活和老龄化的关键作用。工业借调：借调将发生在Simomics办公室，总部设在约克科技园在约克。约克是一个充满活力的学术城市，提供了一个美丽的环境。学生将基于一组在软件开发和计算建模方面具有丰富经验的软件开发人员。