Systems-Mechanobiology of Health and Disease

健康与疾病的系统力学生物学

基本信息

批准号：
MR/T043571/1
负责人：
Fabian Spill
金额：
$ 135.84万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT043571%2F1
关键词：
Systems Mechanobiology Health Disease

项目摘要

Systems biology underpins our success in integrating the wealth of quantitative biological data generated from basic research as well as from studying complex diseases, including the UK's major killers: cancer, cardiovascular and neurodegenerative diseases. Mathematical methodology is critical to achieve this integration, and to develop predictive models that can utilise patient specific data for precision medicine applications, improving diagnostics and optimising personalised treatments.Current systems-biology models focus on the integration of multi-omics data (e.g. genomic and proteomic data), but largely neglect signatures that recent research identified to be of critical importance in driving a large class of diseases: mechanical signatures. Mechanical signatures include stiffened and realigned extracellular matrix, alterations in intracellular forces and obstructions of blood flow. These occur in a broad range of conditions such as solid tumours, atherosclerosis, cardiac fibrosis or liver cirrhosis. Crucially, we now know that these mechanical signatures are sensed by cells and can activate intracellular pathways that may further drive disease development, progression and treatment responses.However, to date, mechanical information is neglected in systems biology. This is mainly due to the lack of mathematical methodologies: systems biology and mechanics are both based on mathematical formalism, yet they were historically developed in isolation by distinct scientific communities. Through this fellowship, I will develop the urgently needed mathematical methodology and then apply it to advance a new class of models that provide fundamental insights into the bi-directional interplay of mechanical and non-mechanical signatures of cells and tissues. To maximise the predictive capabilities of the models, I will form a transdisciplinary research group with modellers and experimentalists working together to develop data-driven models and novel experiments through a robust iterative process. This programme of work will then greatly advance experimental research at the interface of systems - and mechanobiology, the field studying mechanical signatures of biology.In the first four years, I will focus on developing mathematical methodology, models and in-vitro experiments to gain fundamental scientific insights into the interplay of mechanical and non-mechanical signatures of cells and tissues. The focus of this work will be on solid tumours; however, I will engage with experts, e.g. cardiovascular scientists, to test the applicability of my methods to other disease models. Moreover, I will also work closely with a team of experts from biomedical research and the pharmaceutical industry to maximise the translational potential of this work. I will perform specific translational work from year 5 of this project. This work, together with the tailored and comprehensive training programme, will enable me to establish myself as a leader in this newly formed field, systems-mechanobiology. This field will, for the first time, bring together modellers, biologists, clinicians and industry to tackle a wide range of biomedical problems - including cancer, cardiovascular and neurodegenerative diseases and regenerative medicine - through the new systems-mechanobiology approach.

系统生物学是我们成功整合基础研究和复杂疾病研究产生的大量定量生物数据的基础，这些数据包括英国的主要杀手：癌症、心血管和神经退行性疾病。数学方法对于实现这种整合以及开发能够利用患者特定数据进行精准医学应用、改进诊断和优化个性化治疗的预测模型至关重要。当前的系统生物学模型侧重于多组学数据（例如基因组学和基因组学）的整合。蛋白质组数据），但很大程度上忽略了最近研究发现的对驱动一大类疾病至关重要的特征：机械特征。机械特征包括细胞外基质变硬和重新排列、细胞内力的改变和血流阻塞。这些疾病发生在多种疾病中，例如实体瘤、动脉粥样硬化、心脏纤维化或肝硬化。至关重要的是，我们现在知道这些机械特征可以被细胞感知，并且可以激活细胞内通路，从而进一步驱动疾病的发生、进展和治疗反应。然而，迄今为止，机械信息在系统生物学中被忽视。这主要是由于缺乏数学方法论：系统生物学和力学都基于数学形式主义，但它们在历史上是由不同的科学界孤立发展的。通过这项奖学金，我将开发急需的数学方法，然后应用它来推进一类新的模型，为细胞和组织的机械和非机械特征的双向相互作用提供基本见解。为了最大限度地提高模型的预测能力，我将组建一个跨学科研究小组，与建模者和实验者一起工作，通过强大的迭代过程开发数据驱动的模型和新颖的实验。该工作计划将极大地推进系统和机械生物学（研究生物学机械特征的领域）接口的实验研究。在头四年中，我将专注于开发数学方法、模型和体外实验，以获得基础知识对细胞和组织的机械和非机械特征相互作用的科学见解。这项工作的重点将是实体瘤；但是，我会与专家合作，例如心血管科学家，测试我的方法对其他疾病模型的适用性。此外，我还将与生物医学研究和制药行业的专家团队密切合作，最大限度地发挥这项工作的转化潜力。我将从该项目的第五年开始执行具体的翻译工作。这项工作，加上量身定制的综合培训计划，将使我成为系统机械生物学这个新领域的领导者。该领域将首次将建模者、生物学家、临床医生和工业界聚集在一起，通过新的系统机械生物学方法解决广泛的生物医学问题，包括癌症、心血管和神经退行性疾病以及再生医学。