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A CellML simulation compiler and code generator using ODE solving schemes

机译:使用ODE解决方案的CellML仿真编译器和代码生成器

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摘要

Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at .
机译:用描述语言(例如CellML)编写的模型正在成为处理生物功能仿真中复杂细胞生理模型的流行解决方案。但是,为了完全模拟模型,必须将边界条件和常微分方程(ODE)求解方案结合起来。尽管可以在CellML中描述边界条件,但是很难使用现有工具明确指定ODE解决方案。在这项研究中,我们定义了一种基于XML的ODE解决方案描述语言,并提出了一种用于生物功能仿真的代码生成系统。在提出的系统中,可以轻松生成使用各种ODE解决方案的生物仿真程序。我们设计了一种两阶段方法,其中系统生成方程组,该方程组将特定时间t的生理模型变量值与第一阶段中t +Δt的值相关联。第二阶段为模型生成仿真代码。这种方法使得可以灵活构建代码生成模块,从而支持复杂的公式集。我们通过使用各种ODE解决方案模拟复杂的生物学模型来评估模型及其计算精度之间的关系。使用FHN模型仿真,结果显示了与理论预测良好的定性和定量对应。 Luo-Rudy 1991模型的结果表明,仅获得了一阶精度。此外,在GPU上并行运行生成的代码可以将计算时间加快50倍。CellML Compiler源代码可从下载。

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