Cell Modeling

细胞建模

• 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
• 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

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仿真平台， 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 神经元，是深刻的难以理解的，因为空间，生化和 分子复杂性，发生在多个层次的组织，从大分子组装， 神经回路的突触结构。生物学的复杂性令人望而生畏，科学研究人员必须 坚持找到克服它的方法。这很重要，因为科学发现是由可测试的 这些假设来自我们的直觉和围绕我们目前对现实的理解的问题。但 当令人生畏的复杂性混淆了我们的直觉时，我们努力构思新的假设， 发现号慢慢停了下来计算模型使研究人员能够探索复杂的关系 在生物成分之间，获得新的见解和直觉-新假设的起源。的 我们正在开发的用于细胞建模的MCell/CellBlender平台就是专门为满足这一需求而设计的， 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。的 我们的驱动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 和功能以及复杂的生物化学途径参与大脑的学习和记忆。的详细 通过对这些系统的计算建模，对这些系统的理解水平将提供新的 这些见解可能适用于许多类型的细胞信号传导途径，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。