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

基本信息

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
项目类别：
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&apos 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

项目摘要

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（由申请人提供）：使用计算机模拟的多尺度建模与数据挖掘一起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，这将增进对分子和分子之间联系的理解。化学生理学（神经元和星形胶质细胞中的化学信号传导）与电生理学的桥梁提供了必然支持更高组织规模的基础联系，这是特别难以实现的。由于神经细胞的细长性质（与许多其他细胞类型的紧凑细胞相比）以及嵌入尺度的多重重叠（例如，与局部网络处于相同时间和空间尺度的锥体顶端树突域），在神经生物学中进行模拟。开发广泛使用的 NEURON 模拟器，以适应这些有助于信息处理的复杂第二信使信号相互作用的模拟。在之前的资助期间，我们向 NEURON 添加了反应扩散模块，提供了。 3D 确定性扩散与位于细胞质中、内部细胞器上或细胞器内或质膜上的反应相关。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荷，并注意到完整 3D 扩散的细节并不总是如此。作为这些改进的一部分，我们扩展了 NEURON 的 Python 接口，以包含一组专门用于反应扩散建模的新类。此外，我们还准备了用于与 SBML（系统生物学标记）接口的连接器。在当前的提案中，我们将在这些进步的基础上发展涉及 3D 和 1D 随机模拟和确定性模拟组合的“马赛克”模拟，这将涉及在这些不同级别的近似之间轻松切换的能力。这些目标将通过以下具体目标来实现：目标 1. 多种多重网格方法：具有不同尺寸网格的 1D 和 3D 网格目标 2. 使用多分割方法进行并行化，允许单个神经元的模拟在多个处理器上或在单个处理器上的多个线程上运行。目标 3. 使用扩展的 Gillespie 方法进行随机模拟这将补充其他随机方法。目标 4. 传播：用于查看结果、模型开发、模型导入和合并、方法的前端规范的新图形用户界面。比较和多处理器部署。通过课程、教程、示例程序、文档和在线帮助使该工具可供社区使用。