Reproducible development of mathematical models of cardiac electrophysiology

心脏电生理学数学模型的可重复发展

• 批准号: BB/P010008/1
• 负责人: David Gavaghan
• 金额: $ 72.11万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP010008%2F1
• 关键词: Reproducible; development; mathematical; models; cardiac

The aim of "systems biology" is to understand how biological systems (e.g. cells, organs, people) work as a collection of parts by using mathematical modelling. We describe the behaviour of the parts in the form of mathematical equations using the laws of physics and chemistry, and then see how the behaviour of larger systems emerges from this. Many systems biology models for specific components have been published, but there remain significant challenges in exploiting them to understand systems as a whole. Which existing models (if any) are most appropriate for a new scientific question? How does each model behave in different situations? How well do they capture what the real systems do? At present it is very difficult to answer these questions without first downloading each model, writing programs to perform different simulated experiments, and then writing more code to compare and visualize the results. There has been nowhere to look up even simple properties for different models. We propose to build upon a pilot implementation of a system that enables such tasks to be done automatically, with results published on a website.Our approach will be demonstrated in perhaps the most mature area of systems biology: the electrical activity of heart cells, for which the first model was published in 1960 and well over 100 models are now available in public databases. The models have been hugely important in giving insights into how the heart works (and what can go wrong due to disease, age or drug side effects) and have helped in developing new treatments.The first step is to compare the different model predictions to measurements from real cells, to tell us whether we really understand how the heart's cells work. We will link real measurements to our recipes for performing equivalent experiments on the computer models, and provide an interface to display the results, indicating how well they agree both qualitatively (general appearance) and quantitatively (how well the numbers match).Cardiac models often have dozens of equations containing hundreds of parameters - key numbers governing how the models behave. How these parameters were worked out from experimental recordings (data) is, more often than not, unclear. Since many models reuse components from previous models, the original methods and data may no longer be available to anyone. This causes big problems for building on these models - if for instance we want to adapt a model to a new cell type, there is no record of which experiments were performed, or how these were analysed to produce the parameters and equations in the final model. We will extend our recipes to capture this information as well, and so be able automatically to re-calibrate a model to a given set of experiments.Crucially, our tools will use the variability in experimental measurements to calculate how models are likely to need to change to capture variations between different cells in a heart, or between different people, and explain how this variation affects predictions.Three case studies will drive development, looking at different kinds of model to give a broad picture of needs. Feedback from the wider community and an external advisory board of experts in cardiac electrophysiology will also be incorporated, building on the success of our first user workshop in September 2015.The final output will be a user-friendly online system - a "Cardiac Electrophysiology Web Lab" - providing an open community resource for researchers to use. We will also write training materials and run further workshops to help these researchers use it. Our resource will make it easier to reuse or extend existing models in appropriate ways, to develop new models, and to understand differences between heart cells. The tools will increase the impact of modelling for replacing animal experimentation and testing, e.g. in drug trials.

“系统生物学”的目的是通过使用数学建模来理解生物系统（例如细胞，器官，人）如何作为一个部分的集合工作。我们使用物理和化学定律，以数学方程的形式描述部件的行为，然后看看更大系统的行为如何由此产生。许多特定组件的系统生物学模型已经发表，但在利用它们来理解整个系统方面仍然存在重大挑战。哪些现有的模型（如果有的话）最适合一个新的科学问题？每个模型在不同情况下的表现如何？它们对真实的系统的捕获能力如何？目前，如果不首先下载每个模型，编写程序来执行不同的模拟实验，然后编写更多的代码来比较和可视化结果，那么很难回答这些问题。没有任何地方可以查找不同模型的简单属性。我们建议建立在一个系统，使这些任务可以自动完成，结果公布在website. We的方法将被证明在系统生物学的最成熟的领域：心脏细胞的电活动，其中的第一个模型是在1960年出版，现在有超过100个模型在公共数据库。这些模型在深入了解心脏如何工作（以及由于疾病、年龄或药物副作用而可能出现的问题）方面非常重要，并有助于开发新的治疗方法。第一步是将不同的模型预测与真实的细胞的测量结果进行比较，以告诉我们是否真正了解心脏细胞的工作方式。我们将把真实的测量结果与我们在计算机模型上进行等效实验的方法联系起来，并提供一个界面来显示结果，表明它们在定性（总体外观）和定量（数字匹配程度）上的一致程度。心脏模型通常有几十个方程，其中包含数百个参数--决定模型行为的关键数字。这些参数是如何从实验记录（数据）中计算出来的，通常不清楚。由于许多模型重用了以前模型中的组件，因此原始方法和数据可能不再对任何人可用。这给建立这些模型带来了很大的问题-例如，如果我们想让模型适应一种新的细胞类型，没有记录进行了哪些实验，或者如何分析这些实验以产生最终模型中的参数和方程。我们也将扩展我们的方法来捕捉这些信息，从而能够根据给定的一组实验自动重新校准模型。至关重要的是，我们的工具将利用实验测量的可变性来计算模型可能需要如何改变，以捕捉心脏中不同细胞之间或不同人之间的变化，并解释这种变化如何影响预测。三个案例研究将推动开发，研究不同类型的模型，以提供需求的总体情况。在2015年9月我们第一次用户研讨会成功的基础上，还将纳入来自更广泛社区和心脏电生理学专家外部咨询委员会的反馈。最终成果将是一个用户友好的在线系统-“心脏电生理学网络实验室”-为研究人员提供开放的社区资源。我们还将编写培训材料，并举办进一步的研讨会，以帮助这些研究人员使用它。我们的资源将更容易以适当的方式重复使用或扩展现有模型，开发新模型，并了解心脏细胞之间的差异。这些工具将增加建模的影响，以取代动物实验和测试，例如在药物试验中。

项目成果

Models of the cardiac L-type calcium current: A quantitative review.

• DOI: 10.1002/wsbm.1581
• 发表时间: 2023-01
• 期刊: WIREs mechanisms of disease
• 影响因子: 3.1

Personalization of Cellular Electrophysiology Models: Utopia?

细胞电生理学模型的个性化：乌托邦？

• DOI: 10.22489/cinc.2018.063
• 发表时间: 2018
• 作者: Clerx M

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data

过滤器推理：针对快照时间序列数据的可扩展非线性混合效应推理方法

• DOI: 10.1101/2022.11.01.514702
• 发表时间: 2022
• 作者: Augustin D

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

• DOI: 10.1371/journal.pcbi.1011135
• 发表时间: 2023-05
• 期刊: PLoS computational biology
• 影响因子: 4.3

Four ways to fit an ion channel model

拟合离子通道模型的四种方法

• DOI: 10.1101/609875
• 发表时间: 2019
• 作者: Clerx M