Computational Biology, Department of Computer Science, University of Oxford, Oxford, UK.
Research IT Services, University College London, London, UK.
Prog Biophys Mol Biol. 2018 Nov;139:3-14. doi: 10.1016/j.pbiomolbio.2018.05.011. Epub 2018 May 26.
The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.
心脏细胞电生理学建模是系统生物学中最成熟的领域之一。这种集中的研究投入带来了新的挑战,其中最重要的是选择哪种模型最适合解决特定的科学问题。在之前的一篇论文中,我们介绍了我们在开发一个在线资源方面的初步工作,该资源用于在广泛的实验场景中对电生理细胞模型进行特征描述和比较。在那项工作中,我们描述了如何开发一种新的协议语言,使我们能够将数学模型的细节(大多数心脏细胞模型采用常微分方程的形式)与正在模拟的实验方案分开。我们开发了一个完全开放的在线存储库(我们称之为心脏电生理学网络实验室),允许用户存储和比较将相同实验方案应用于竞争模型的结果。在当前的论文中,我们描述了这项工作的最新和计划扩展,重点是支持从实验数据构建模型的过程。我们概述了开发一种可用于描述从湿实验室数据集推断参数的机器可读语言的必要工作,并通过使用实验数据拟合 hERG 通道模型的详细示例来说明我们的方法。最后,我们讨论了在朝着我们的目标取得进一步进展方面的未来挑战,以实现心脏细胞模型开发的完全可重现方法。