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（由应用程序提供）：使用计算机模拟的多尺度建模越来越多地被认为是一种主要方法，以及数据挖掘，用于吸收系统生物学中广阔而不断增长的知识基础。这将提高人们对分子与疾病表现之间的联系的理解，以转化为诊所。与电生理学的化学生理学（神经元和星形胶质细胞中的化学信号传导）的桥接提供了基本的联系，这必然支持更高的组织量表。多个量表模型特别困难地在神经生物学DU中模拟神经元细胞的细长性质（与许多其他细胞类型的紧凑细胞相比），以及与本地网络相同的临时和空间尺度上的嵌入式量表的多个重叠（例如，锥体顶端树突状结构量）。我们正在开发广泛使用的神经元模拟器，以适应有助于信息处理的这些复杂的第二理由信号相互作用。在以前的资金期间，我们将反应扩散模块添加到神经元中，提供了与位于细胞质，内部细胞器或内部细胞器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，并指出完整3D扩散的细节并不总是需要。作为这些改进的一部分，我们将神经元的Python界面扩展到包括部署到反应扩散建模的一组新类。此外，我们准备了与SBML接口的连接器（系统生物学标记语言）。在当前的提案中，我们将基于这些进步，以允许开发“马赛克”模拟，涉及3D和1D中随机模拟的组合。这将涉及在这些不同级别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特定目的来实现：目标1。多个多机方法：1D和3D网格在不同位置具有不同尺寸的网格。 AIM 2。使用多种方法并行化，允许模拟单个神经元在多个处理器上或单个处理器上的多个线程进行模拟。 AIM 3。使用扩展的Gillespie方法的随机模拟。这将补充其他随机方法，这些随机方法也将在神经元中提供。 AIM 4。传播：用于查看结果，模型开发，模型导入和合并的前端规范的新图形用户界面，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。