Cell Modeling

细胞建模

基本信息

批准号：
10228748
负责人：
TERRENCE J SEJNOWSKI
金额：
$ 41.31万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-24 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10228748
关键词：
3-Dimensional Address Algorithms Architecture Automobile Driving Biochemical Biochemical Pathway Biochemistry Biological Biological Models Brain Cell model Cells Cloud Computing Code Complex Computer Models Computer Simulation Coupling Data Set Development Development Plans Electrons Evaluation Event Functional disorder Funding Geometry Goals High Performance Computing Hybrids Image Internet Intuition Knowledge Language Learning Memory Methods Modeling Modification Molecular Network-based Neurologic Neurons Pathology Phase Physics Preparation Process Pythons Reaction Research Research Personnel Research Project Grants Running Schedule Signal Pathway Signal Transduction Space Models Structure Synapses System Technology Time Update Vision Work advanced simulation base biological systems cell type computing resources design flexibility image registration improved insight macromolecular assembly meetings microscopic imaging model building model development models and simulation multi-scale modeling multithreading neural circuit neurophysiology next generation parallelization particle simulation success tool

项目摘要

IV. TR&D2 - Abstract The overall goal of this project is to develop the next generation computer simulation platform for spatially realistic simulation and analysis of cellular and subcellular biochemistry. Cellular systems, especially in neurons, are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Biological complexity is daunting and scientific investigators must persevere to finds ways to overcome it. This is important because Scientific Discovery is driven by testable hypotheses which derive from our intuition and questions surrounding our current understanding of reality. But when daunting complexity confounds our intuition we struggle to conceive new hypotheses and the cycle of discovery grinds to a halt. Computational models allow investigators to probe the complex relationships between biological components, obtain new insights and intuition -- the genesis of new hypotheses. The MCell/CellBlender platform for cell modeling we are developing is expressly designed to fulfill this need, providing insight and understanding of complex cellular systems. The cell modeling tools we develop here are designed to mesh with the molecular, network, and image-derived modeling tools of TR&Ds 1, 3 and 4. The tools will be used by our Driving Biomedical Project research partners to study neuronal and synaptic structure and function and the intricate biochemical pathways involved in learning and memory in the brain. The detailed level of understanding of these systems afforded by computational modeling of these systems will provide new insights that may be applicable to many types of cell signaling pathways, and in particular should help to elucidate how dysfunctions in cell signaling may contribute to neurological and psychiatric pathology.

四． TR&D2 - 摘要该项目的总体目标是开发下一代空间计算机模拟平台细胞和亚细胞生物化学的真实模拟和分析。蜂窝系统，特别是在由于空间、生物化学和神经元之间的相互作用，神经元非常难以理解。分子复杂性发生在组织的多个层面上，从大分子组装到突触架构到神经回路。生物的复杂性令人望而生畏，科学研究人员必须坚持寻找克服它的方法。这很重要，因为科学发现是由可测试的驱动的源自我们的直觉的假设以及围绕我们当前对现实的理解的问题。但当令人畏惧的复杂性混淆了我们的直觉时，我们很难构想新的假设和循环发现逐渐停止。计算模型使研究人员能够探究复杂的关系生物成分之间，获得新的见解和直觉——新假设的起源。这我们正在开发的用于细胞建模的 MCell/CellBlender 平台专门为满足这一需求而设计，提供对复杂细胞系统的洞察和理解。我们在这里开发的细胞建模工具是旨在与 TR&D 1、3 和 4 的分子、网络和图像衍生建模工具相结合。我们的驾驶生物医学项目研究合作伙伴将使用这些工具来研究神经元和突触结构大脑的学习和记忆所涉及的功能和复杂的生化途径。详细的这些系统的计算建模所提供的对这些系统的理解水平将提供新的可能适用于多种类型的细胞信号传导途径的见解，特别是应该有助于阐明细胞信号传导功能障碍如何导致神经和精神病理学。