Computational Infrastructure for Multiscale Modelling in Biomedical Engineering

生物医学工程多尺度建模的计算基础设施

• 批准号: EP/F033710/1
• 负责人: Yiannis Ventikos
• 金额: $ 11.16万
• 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: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF033710%2F1
• 关键词: Computational; Infrastructure; Multiscale; Modelling; Biomedical

Computational modelling is one of the pillars of scientific research in general and biomedical engineering in particular. Simulating real human body functions and organs on the computer presents us with a host of very favourable features:-Computer simulation is totally non-invasive and therefore 100% safe. At an era where the demand for safer, more effective and more economical treatments is becoming increasingly pressing, computer modelling offers an ideal alternative to all other types of testing (animal, clinical etc.): Where applicable, it allows for a major paradigm shift: all testing takes place on the computer, completely remote from testing on living beings. -Moreover, modelling is perfectly repeatable. This feature is very appealing to scientific research, because it allows for experiments (in this case, virtual experiments, on the computer) to be repeated as many times as necessary, in identical and perfectly controllable conditions: Uncertainties and imperfections, all unavoidable features of physical testing and experimentation, are minimised or eliminated. -These two features lead inevitably to a third characteristic of computational modelling: once the tools are in place, the cost of conducting virtual experiments is minimal or zero. This is in great contract to all other types of testing.The main caveat in computational experimentation of the type discussed is that the models have to be developed first and they need to be comprehensive, accurate and validated as to represent reality. The resources we request in this proposal aim at providing us with equipment that is necessary for the development of such models, as they are very demanding in terms of computational resources. To exemplify, we could mention that in order to simulate blood flow in a diseased artery, a computation that would take about one month on a very powerful personal computer is needed. This time-frame is unacceptable in clinical practice: decisions on disease progress and treatment strategy have to be made within a day or two. This excessive time requirement is equally problematic in the model development and validation stage, i.e. the effort the members of our group get involved in. Model development involves many test computations and rapid turn-around time is essential for evaluating the efficacy of specific techniques, the identification and correction of errors, the improvement of model realism, integration etc. Fortunately, computer technology provides us with an affordable way to increase dramatically the speed of such computations: instead of depending on a single computer for such simulations, we can split the problem at hand into many small tasks and assign each one of those tasks to a separate computer; all those machines communicate with each other over a dedicated fast network and exchange all the information needed. This approach (parallelisation) is the dominating trend in high performance computing nowadays.In the example mentioned before, if the computation that would take one month in a single computer is distributed to, say, 30 identical computers, the time required for the simulation to complete is approximately 1 day (usually it is slightly more because parallelisation is never perfect due to communication overheads), a turn-around time that is perfectly acceptable both for clinical decision-making and for effective algorithm development.We request resources to purchase a computer cluster that will enable us to improve dramatically our simulation methods development cycle and will allow us to conduct research that is more challenging and more useful to our clinical partners. The researchers in the group (mostly doctoral students) shall be able to enhance their productivity and will be able to tackle problems that are currently out of reach. In summary, this proposal will open new roads for us, roads that will allow us to model diseases and treatments that we cannot address right now.

计算模型是科学研究的支柱之一，尤其是生物医学工程。在计算机上模拟真实的人体功能和器官为我们提供了许多非常有利的特征：-计算机模拟是完全非侵入性的，因此是100%安全的。在一个对更安全、更有效、更经济的治疗方法的需求日益迫切的时代，计算机建模为所有其他类型的测试(动物、临床等)提供了理想的替代方案：在适用的情况下，它允许重大范式转变：所有测试都在计算机上进行，完全远离对生物的测试。-此外，建模是完全可重复的。这一特征对科学研究非常有吸引力，因为它允许在相同和完全可控的条件下，尽可能多次地重复实验(在这种情况下，计算机上的虚拟实验)：将物理测试和实验中所有不可避免的特征--不确定和不完美--最小化或消除。-这两个特征不可避免地导致了计算建模的第三个特征：一旦工具到位，进行虚拟实验的成本最低或为零。这与所有其他类型的测试大相径庭。在讨论的这类计算实验中，主要的警告是必须首先开发模型，并且它们需要全面、准确和验证以代表现实。我们在本提案中要求的资源旨在为我们提供开发这种模型所需的设备，因为它们在计算资源方面要求非常高。为了举例说明，我们可以提到，为了模拟病变动脉中的血液流动，需要在一台非常强大的个人计算机上进行大约一个月的计算。这一时间框架在临床实践中是不可接受的：关于疾病进展和治疗策略的决定必须在一两天内做出。这种过多的时间要求在模型开发和验证阶段也是同样有问题的，也就是我们小组成员参与的工作。模型开发涉及许多测试计算，快速周转时间对于评估特定技术的有效性、识别和纠正错误、改进模型真实性、集成等至关重要。幸运的是，计算机技术为我们提供了一种负担得起的方式来大幅提高此类计算的速度：我们可以将手头的问题拆分成许多小任务，并将其中的每一项任务分配给单独的计算机；所有这些机器通过专用的快速网络相互通信，并交换所需的所有信息。这种方法(并行化)是当今高性能计算的主导趋势。在前面提到的例子中，如果将一台计算机上需要一个月的计算分配给比如30台相同的计算机，完成模拟所需的时间大约是1天(通常略多一些，因为并行化由于通信开销而从来都不是完美的)，这一周转时间对于临床决策和有效的算法开发都是完全可以接受的。我们请求资源购买计算机集群，这将使我们能够显著改进我们的模拟方法开发周期，并允许我们进行更具挑战性和对临床合作伙伴更有用的研究。小组中的研究人员(主要是博士生)应该能够提高他们的生产力，并能够解决目前遥不可及的问题。总而言之，这项提议将为我们开辟新的道路，这些道路将使我们能够模拟目前无法解决的疾病和治疗方法。

项目成果

Computational modelling for cerebral aneurysms: risk evaluation and interventional planning.

• DOI: 10.1259/bjr/14303482
• 发表时间: 2009
• 期刊: The British journal of radiology
• 作者: Y. Ventikos;E. C. Holland;T. Bowker;P. Watton;Nikolaos M. P. Kakalis;Mustafa Megahed;F. Zhu;Paul Su

Chemosignalling, mechanotransduction and ciliary behaviour in the embryonic node: Computational evaluation of competing theories

胚胎节点的化学信号传导、机械传导和纤毛行为：竞争理论的计算评估

• DOI: 10.1177/0954411914531117
• 发表时间: 2014-05-01
• 期刊: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
• 影响因子: 1.8
• 作者: Chen, Duanduan;Norris, Dominic;Ventikos, Yiannis
• 通讯作者: Ventikos, Yiannis

Exploring the efficacy of endoscopic ventriculostomy for hydrocephalus treatment via a multicompartmental poroelastic model of CSF transport: a computational perspective.

• DOI: 10.1371/journal.pone.0084577
• 发表时间: 2013
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Vardakis JC;Tully BJ;Ventikos Y
• 通讯作者: Ventikos Y

Investigating cerebral oedema using poroelasticity.

• DOI: 10.1016/j.medengphy.2015.09.006
• 发表时间: 2016
• 期刊: Medical engineering & physics
• 影响因子: 2.2
• 作者: J. Vardakis;D. Chou;B. Tully;Chang-Chi Hung;Tsong-Hai Lee;P. Tsui;Y. Ventikos

Interaction of a strong shockwave with a gas bubble in a liquid medium: a numerical study

• DOI: 10.1017/jfm.2012.132
• 发表时间: 2012-06-25
• 期刊: JOURNAL OF FLUID MECHANICS
• 影响因子: 3.7
• 作者: Hawker, N. A.;Ventikos, Y.
• 通讯作者: Ventikos, Y.