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.

系统生物学基于我们在整合基础研究以及研究复杂疾病（包括英国的主要杀手：癌症，心血管和神经退行性疾病）中产生的大量定量生物学数据方面的成功。 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.机械特征包括僵硬和重新调整的细胞外基质，细胞内力的改变以及血流的障碍物。这些发生在各种疾病中，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是通过细胞感知的，并且可以激活可能进一步推动疾病发展，进展和治疗反应的细胞内途径。迄今为止，在系统生物学中忽略了机械信息。这主要是由于缺乏数学方法论：系统生物学和力学都是基于数学形式主义的，但是历史上它们是由不同的科学社区孤立地开发的。通过这项奖学金，我将开发急需的数学方法论，然后将其应用于新的模型，从而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的见解。为了最大程度地提高模型的预测能力，我将组成一个跨学科研究小组，由建模者和实验者共同努力，通过强大的迭代过程共同开发数据驱动的模型和新型实验。然后，该工作计划将在系统的界面和机械生物学，研究生物学的机械特征的现场上大大提高实验研究。在最初的四年中，我将着重于开发数学方法论，模型和维特罗实验，以获得对细胞和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织和组织的基本洞察力的基本洞察力。这项工作的重点将放在实体瘤上。但是，我将与专家互动，例如心血管科学家，以测试我的方法对其他疾病模型的适用性。此外，我还将与来自生物医学研究和制药行业的专家团队紧密合作，以最大程度地发挥这项工作的转化潜力。我将从该项目的第五年进行特定的翻译工作。这项工作以及量身定制和全面的培训计划将使我能够确立自己在这个新成立的领域，系统 - 机械生物学中的领导者。该领域将首次通过新的Systems -Mechanobiology方法将模块，生物学家，临床医生和行业汇总在一起，以解决各种生物医学问题，包括癌症，心血管和神经退行性疾病以及再生医学。