Modeling and analysis of material transport in complex geometry of neurons

神经元复杂几何形状中物质传输的建模和分析

• 批准号: 1804929
• 负责人: Yongjie Zhang
• 金额: $ 40万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1804929&HistoricalAwards=false
• 关键词: Modeling; analysis; material; transport; complex

To survive and function, neurons must transport essential materials down long projections called neurites. Neurites bring information to and from other neurons. The transport of materials down neurites must be controlled to make sure that the right type and amount of material is delivered to the right destination. Using new engineering models, methods, and software, this project's goal is to explain how material traffic is routed and balanced in the complex geometry of neurons. This will enhance understanding of how neurons operate their material transport systems and, more importantly, how to control neuronal structure and function. Successful completion of this project will 1) advance fundamental knowledge of neurobiology and neural engineering on how materials are transported in the complex geometry of neurons; 2) provide new insights into mechanisms of material transport related to neurological diseases such as Alzheimer's disease; and 3) produce software required for developing related drug delivery solutions. The engineering tools developed will be distributed freely and openly in order to further advance related basic and translational research. Research in this project will be closely integrated with teaching to provide students with interdisciplinary training opportunities. Educational materials related to basic knowledge of neurobiology and neural engineering will be developed and disseminated to the public through the internet as well as through local education and outreach activities.The goal of this project is to elucidate how material transport traffic is routed and balanced in the complex geometry of neurons through developing and applying new engineering models, methods and software. Studies are designed to test the hypothesis that traffic routing and balancing are actively controlled based on the local geometry of the complex neurite network. The research plan has three aims. 1) To determine how traffic is routed within the neurite network. Network geometry and transport patterns will be obtained by collecting time-lapse movies of transport of amyloid precursor and synaptic vesicle proteins and mitochondria at neurite junctions of drosophila sensory neurons and rat hippocampal neurons and counting the number of cargoes going into different branches. Data analysis features include developing neurite tracing software, vector characterization of traffic routing distributions at each branch, testing different traffic routing models based on different assumptions of the cytoskeletal structure at the junctions, and using single particle tracking. The imaging and data analysis techniques developed will then be applied to determining if damage of specific neurite branches (laser ablation) and the Alzheimer's condition (amyloid-beta peptide induced) will influence traffic routing. 2) To determine how traffic is balanced within the neurite network. The topological structure of the network will be represented as trees and the theoretical framework for understanding traffic balance will be inspired by flux balance analysis of metabolic networks. The focus will be on how traffic is balanced in single branches and subnetworks. Comparable to Aim 1, studies will be performed to determine how traffic is balanced in damaged and Alzheimer's disease neurons. Computer simulations will be used to understand relations between traffic routing and balancing. 3) To develop and apply open-source software for computer simulation of material transport in complex 3D geometry of neurons. A new isogeometric analysis (IGA) based numerical technique will be developed to simulate material transport within the complex geometry. The simulation software will be validated and tested through integration with experiments and then used to design intracellular delivery strategies for related neurological diseases whose geometries can be obtained in existing databases. The results of this project have the potential to advance both neurobiology and neuroengineering fields; neurobiology advances come in the form of new understanding of the structure and function of neurons; neuroengineering advances come in the form of new understanding about how to utilize and control the material transport process for applications such as repair and renewal of damaged or degenerative neurons. The image acquisition, data analysis and modeling tools developed may be widely applicable in other areas of investigation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了生存和发挥功能，神经元必须沿着称为神经突的长突起运输必需的物质。神经突将信息传入和传出其他神经元。 必须控制材料沿着神经突的运输，以确保将正确类型和数量的材料输送到正确的目的地。 该项目的目标是使用新的工程模型、方法和软件来解释物质流量如何在神经元的复杂几何形状中路由和平衡。 这将增强对神经元如何操作其物质运输系统的理解，更重要的是，如何控制神经元的结构和功能。 该项目的成功完成将：1）提高神经生物学和神经工程的基础知识，了解材料如何在神经元的复杂几何形状中运输； 2）为与阿尔茨海默病等神经系统疾病相关的物质运输机制提供新的见解； 3) 生产开发相关药物输送解决方案所需的软件。开发的工程工具将自由、公开地分发，以进一步推进相关的基础和转化研究。该项目的研究将与教学紧密结合，为学生提供跨学科的锻炼机会。将开发与神经生物学和神经工程基础知识相关的教育材料，并通过互联网以及当地教育和外展活动向公众传播。该项目的目标是阐明物质交通运输在城市中如何路由和平衡。通过开发和应用新的工程模型、方法和软件来研究神经元的复杂几何形状。 研究旨在测试基于复杂神经突网络的局部几何形状主动控制流量路由和平衡的假设。 该研究计划有三个目标。 1) 确定神经突网络内流量的路由方式。 通过收集果蝇感觉神经元和大鼠海马神经元神经突连接处淀粉样蛋白前体和突触小泡蛋白和线粒体运输的延时电影，并计算进入不同分支的货物数量，获得网络几何结构和运输模式。 数据分析功能包括开发神经突追踪软件、每个分支的交通路由分布的矢量表征、基于交叉点细胞骨架结构的不同假设测试不同的交通路由模型，以及使用单粒子追踪。 开发的成像和数据分析技术将用于确定特定神经突分支的损伤（激光消融）和阿尔茨海默病（β-淀粉样肽诱导）是否会影响交通路由。 2) 确定神经突网络内的流量如何平衡。 网络的拓扑结构将被表示为树，理解流量平衡的理论框架将受到代谢网络的通量平衡分析的启发。 重点将放在如何平衡单个分支和子网中的流量。 与目标 1 类似，我们将进行研究以确定受损神经元和阿尔茨海默病神经元中的交通如何平衡。 计算机模拟将用于理解流量路由和平衡之间的关系。 3) 开发和应用开源软件，用于计算机模拟神经元复杂 3D 几何形状中的物质传输。 将开发一种基于等几何分析（IGA）的新数值技术来模拟复杂几何形状内的材料传输。 该模拟软件将通过与实验结合进行验证和测试，然后用于设计相关神经系统疾病的细胞内递送策略，其几何形状可以在现有数据库中获得。该项目的结果有可能推动神经生物学和神经工程领域的发展；神经生物学的进步表现为对神经元结构和功能的新理解；神经工程的进步表现为对如何利用和控制材料传输过程以实现受损或退行性神经元的修复和更新等应用的新理解。所开发的图像采集、数据分析和建模工具可广泛应用于其他研究领域。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Interpolatory Curve Modeling with Feature Points Control

具有特征点控制的插值曲线建模

• DOI: 10.1016/j.cad.2019.05.010
• 发表时间: 2019-09
• 期刊: Computer-Aided Design
• 影响因子: 4.3
• 作者: Chen Zhonggui;Huang Jinxin;Cao Juan;Zhang Yongjie Jessica
• 通讯作者: Zhang Yongjie Jessica

DTHB3D_Reg: Dynamic Truncated Hierarchical B-Spline Based 3D Nonrigid Image Registration

• DOI: 10.4208/cicp.oa-2017-0141
• 发表时间: 2018
• 期刊: Communications in Computational Physics
• 影响因子: 3.7
• 作者: Aishwarya Pawar;Y. Zhang;C. Anitescu;Yue Jia;T. Rabczuk

CVT-based 3D image segmentation and quality improvement of tetrahedral/hexahedral meshes using anisotropic Giaquinta-Hildebrandt operator

• DOI: 10.1080/21681163.2016.1244017
• 发表时间: 2018-05
• 期刊: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
• 作者: Kangkang Hu;Y. Zhang;Guoliang Xu

The divergence-conforming immersed boundary method: Application to vesicle and capsule dynamics

• DOI: 10.1016/j.jcp.2020.109872
• 发表时间: 2021-01-15
• 期刊: JOURNAL OF COMPUTATIONAL PHYSICS
• 影响因子: 4.1
• 作者: Casquero, Hugo;Bona-Casas, Carles;Zhang, Yongjie Jessica
• 通讯作者: Zhang, Yongjie Jessica

Reaction diffusion system prediction based on convolutional neural network

• DOI: 10.1038/s41598-020-60853-2
• 发表时间: 2020-03-03
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Li, Angran;Chen, Ruijia;Zhang, Yongjie Jessica
• 通讯作者: Zhang, Yongjie Jessica