Extension of NEURON simulator for simulation of reaction-diffusion in neurons

 描述（由申请人提供）：使用计算机模拟的多尺度建模越来越被认为是一种主要方法，沿着数据挖掘，用于吸收系统生物学中庞大且不断增长的知识库。这将提高对分子与疾病表现之间联系的理解，以便将研究转化为临床。化学生理学（神经元和星形胶质细胞中的化学信号）与电生理学的桥梁提供了一个基本的联系，这将必然支撑更高的组织规模。由于神经细胞的伸长性质（与许多其他细胞类型的紧凑细胞相比）以及嵌入尺度的多个重叠（例如，在与局部网络相同的时间和空间尺度上的锥体顶端树突域）。我们正在开发广泛使用的神经元模拟器，以适应这些复杂的第二信使信号的相互作用，有助于信息处理的模拟。在上一个资助期，我们将反应扩散模块添加到NEURON中，提供与位于细胞质中，内部细胞器上或内部细胞器内或质膜上的反应相关的3D确定性扩散。我们还添加了1D确定性扩散，以减少限制模拟范围的高计算负载，并注意到并不总是需要完整3D扩散的细节。作为这些改进的一部分，我们扩展了NEURON的Python接口，以包括一组专门用于反应扩散建模的新类。此外，我们还准备了与SBML（系统生物学标记语言）接口的连接器。在目前的建议中，我们将建立在这些进步，以允许“马赛克”模拟的发展，涉及在3D和1D的随机和确定性模拟的组合。这将涉及在这些不同近似水平之间容易切换的能力，以便可以比较不同的建模方法。这些目标将通过以下具体目标实现：目标1。多种多重网格方法：1D和3D网格在不同位置具有不同大小的网格。目标二。使用多分裂方法的并行化，允许单个神经元的模拟跨多个处理器或单个处理器上的多个线程运行。目标3。使用扩展的吉莱斯皮方法进行随机模拟。这将补充其他随机方法，这些方法也将在NEURON中提供。目标4。传播：新的图形用户界面用于前端规范，用于查看结果、模型开发、模型导入和合并、方法比较和多处理器部署。通过课程、教程、示例程序、文档和在线帮助使社区可以访问该工具。

 DESCRIPTION (provided by applicant): Multiscale modeling using computer simulation is increasingly recognized as a major method, along with data-mining, for assimilating the vast and ever-growing knowledge base in systems biology. This will improve understanding of the links between molecules and disease manifestation for translational research to the clinic. The bridging of chemophysiology (chemical signaling in neurons and astrocytes) with electrophysiology provides a fundamental connection that will necessarily underpin higher organizational scales. Multiscale models are particularly difficult to simulate in neurobiology du to the elongated nature of neural cells (compared to compact cells for many other cell types), and to multiple overlapping of embedded scales (e.g., pyramidal apical dendrite domains at the same temporal and spatial scale as local networks). We are developing the widely used NEURON simulator to accommodate simulation of these complex second-messenger signal interactions that contribute to information processing. In the prior funding period, we added the reaction-diffusion module to NEURON, providing 3D deterministic diffusion linked to reactions situated in cytosol, on or within internal organelles, or on plasma membrane. We also added 1D deterministic diffusion to reduce high computational loads that limited the scope of simulations, noting that the detail of full 3D diffusion is not always needed. As part of these improvements, we extended NEURON's Python interface to include a new set of classes devoted to reaction-diffusion modeling. Additionally, we prepared connectors for interfacing with SBML (Systems Biology Markup Language). In the current proposal, we will build on these advances in order to allow development of "mosaic" simulations involving combinations of stochastic and deterministic simulation in both 3D and 1D. This will involve the ability to readily switch among these different levels of approximation so that different modeling approaches can be compared. These objectives will be achieved through the following Specific Aims: Aim 1. Multiple multigrid methods: 1D and 3D grids with different sized grids at different locations. Aim 2. Parallelization using multisplit methods that allow the simulation of a single neuron to be run across multiple processors or across multiple threads on a single processor. Aim 3. Stochastic simulation using an extended Gillespie method. This will complement additional stochastic methods that will also be made available in NEURON. Aim 4. Dissemination: new Graphical User Interface for front-end specifications for viewing results, model development, model importation and merging, method comparison and multiprocessor deployment. Making the tool accessible to the community via courses, tutorials, example programs, documentation and online help.