Systems-Mechanobiology of Health and Disease

健康与疾病的系统力学生物学

• 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

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大量定量生物学数据的基础，这些数据来自基础研究和复杂疾病的研究，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和开发预测模型至关重要，这些模型可以利用患者特定数据进行精准医疗应用，改善诊断和优化个性化治疗。当前的系统生物学模型侧重于多组学数据的整合（例如基因组和蛋白质组学数据），但在很大程度上忽视了最近研究发现的在驱动一大类疾病中至关重要的特征：机械特征。机械特征包括细胞外基质变硬和重新排列，细胞内力改变和血流阻塞。这种情况发生在多种情况下，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，并且可以激活可能进一步驱动疾病发展、进展和治疗反应的细胞内通路。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：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团体独立发展的。通过这个奖学金，我将发展急需的数学方法，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的见解。为了最大限度地提高模型的预测能力，我将与建模人员和实验人员组成一个跨学科研究小组，通过一个强大的迭代过程，共同开发数据驱动的模型和新颖的实验。这一工作计划将极大地推进系统和机械生物学（研究生物学的机械特征的领域）界面的实验研究。在最初的四年里，我将专注于发展数学方法，模型和体外实验，以获得对细胞和组织的机械和非机械特征相互作用的基本科学见解。这项工作的重点是实体肿瘤；然而，我将与专家，如心血管科学家合作，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密切合作，以最大限度地发挥这项工作的转化潜力。我将从这个项目的第5年开始进行具体的翻译工作。这项工作，加上量身定制的全面培训计划，将使我成为系统机械生物学这一新兴领域的领导者。这一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-机械生物学方法来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。

项目成果

Discretised flux balance analysis for reaction-diffusion simulation of single-cell metabolism

• DOI: 10.1101/2023.08.01.551453
• 发表时间: 2024-01
• 期刊: bioRxiv
• 作者: Yin Hoon Chew;F. Spill

A mechanical modelling framework to study endothelial permeability

研究内皮渗透性的机械建模框架

• DOI: 10.1101/2023.07.28.551049
• 发表时间: 2023
• 作者: Keshavanarayana P

Modeling the three-way feedback between cellular contractility, actin polymerization, and adhesion turnover resolves the contradictory effects of RhoA and Rac1 on endothelial junction dynamics

对细胞收缩性、肌动蛋白聚合和粘附周转之间的三向反馈进行建模，解决了 RhoA 和 Rac1 对内皮连接动力学的矛盾影响

• DOI: 10.1101/2021.03.15.435512
• 发表时间: 2021
• 作者: McEvoy E

Effects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy - an in-silico study

细胞超微结构改变对糖尿病心肌病能量代谢的影响——一项计算机研究

• DOI: 10.1101/2022.05.22.492785
• 发表时间: 2022
• 作者: Ghosh S

Endothelium and Subendothelial Matrix Mechanics Modulate Cancer Cell Transendothelial Migration.

• DOI: 10.1002/advs.202206554
• 发表时间: 2023-06
• 期刊: ADVANCED SCIENCE
• 影响因子: 15.1
• 作者: Javanmardi, Yousef;Agrawal, Ayushi;Malandrino, Andrea;Lasli, Soufian;Chen, Michelle;Shahreza, Somayeh;Serwinski, Bianca;Cammoun, Leila;Li, Ran;Jorfi, Mehdi;Djordjevic, Boris;Szita, Nicolas;Spill, Fabian;Bertazzo, Sergio;Sheridan, Graham K.;Shenoy, Vivek;Calvo, Fernando;Kamm, Roger;Moeendarbary, Emad
• 通讯作者: Moeendarbary, Emad