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

用于模拟神经元反应扩散的神经模拟器的扩展

• 批准号: 9893029
• 负责人: William W Lytton
• 金额: $ 40.8万
• 依托单位: SUNY DOWNSTATE MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9893029
• 关键词: 3-Dimensional; Algorithms; Alzheimer' s Disease; Apical; Area; Astrocytes; Biochemical; Brain; Brain Diseases; Cell membrane; Cells; Cellular biology; Chemicals; Clinic; Code; Communities; Complement; Complex; Computer Simulation; Computers; Cytosol; Dendrites; Development; Differential Equation; Diffusion; Disease; Documentation; Educational workshop; Electrophysiology (science); Environment; Funding; Genomics; Language; Laws; Learning; Link; Location; Memory; Mental disorders; Methods; Mind; Modeling; Molecular; Molecular Biology; Monte Carlo Method; Mosaicism; Nature; Nerve; Nerve Fibers; Neurobiology; Neurons; Organ; Organelles; Performance; Pharmacotherapy; Proteomics; Pythons; Reaction; Resources; Running; Schizophrenia; Second Messenger Systems; Signal Transduction; Speed; Stochastic Processes; System; Systems Biology; Techniques; Technology; Time; Training; Translational Research; Travel; Variant; autism spectrum disorder; cell type; chemical reaction; computational neuroscience; data mining; design; graphical user interface; high end computer; improved; information processing; knowledge base; model design; model development; multi-scale modeling; multicore processor; neglect; nervous system disorder; open source; parallelization; phenomenological models; programs; public health relevance; relating to nervous system; simulation; simulation environment; tool; voltage

 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.

 描述(申请人提供)：与数据挖掘一起，使用计算机模拟的多尺度建模越来越被认为是吸收系统生物学中庞大且不断增长的知识库的主要方法。这将提高对分子和疾病表现之间的联系的理解，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)与电生理学之间的桥梁提供了一个基本的联系，这必然会支持更高的组织规模。多尺度模型在神经生物学中尤其难以模拟，因为神经细胞的拉长性质(与许多其他细胞类型的致密细胞相比)，以及嵌入尺度的多重重叠(例如，与局部网络处于相同时间和空间尺度的金字塔顶端树突结构域)。我们正在开发广泛使用的神经元模拟器，以适应对这些复杂的第二信使信号相互作用的模拟，这些信号相互作用有助于信息处理。在之前的资助期间，我们向神经元添加了反应-扩散模块，提供与位于胞浆、内细胞器上或细胞器内或质膜上的反应相联系的3D确定性扩散。我们还添加了一维确定性扩散，以减少限制模拟范围的高计算量，并注意到并不总是需要完整的三维扩散的细节。作为这些改进的一部分，我们扩展了神经元的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。在目前的提案中，我们将在这些进展的基础上，开发涉及3D和1D的随机和确定性模拟组合的“马赛克”模拟。这将涉及到在这些不同的近似级别之间轻松切换的能力，以便可以比较不同的建模方法。这些目标将通过以下具体fic目标来实现：目标1.多种多重网格方法：在不同位置使用不同大小的网格的一维和三维网格。目标2.使用多分裂方法的并行化，该方法允许在单个处理器上跨多个处理器或跨多个线程运行单个神经元的模拟。目的3.用扩展的吉列斯皮方法进行随机模拟。这将补充其他随机方法，这些方法也将在神经元中使用。目标4.传播：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的前端规格fi阳离子的新图形用户界面。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。