权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Analysis Tools for Fiber Diffraction of Muscle

肌肉纤维衍射分析工具

基本信息

批准号：
10630905
负责人：
THOMAS C IRVING
金额：
$ 34.68万
依托单位：
ILLINOIS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10630905
关键词：
Acceleration Address Adoption Algorithms Animal Model Area Artificial Intelligence Biophysics Biopsy Cardiomyopathies Center Core Grants Code Communities Computer Simulation Computer software Contracts Data Data Analyses Data Set Development Diffuse Disease Documentation Fiber Funding Future Generations Goals Grant Heart Diseases Human Inherited Laboratories Methodology Methods Microfilaments Modeling Molecular Muscle Muscle Fibers Muscle function Mutation Myopathy Myosin ATPase National Institute of Arthritis, and Musculoskeletal, and Skin Diseases National Institute of General Medical Sciences Pattern Performance Persons Phenotype Photons Physiological Positioning Attribute Procedures Production Productivity Property Publishing Regulation Reproducibility Resolution Resources Roentgen Rays Sarcomeres Skeletal Muscle Source Striated Muscles Structure Synchrotrons Techniques Technology Testing Thick Filament Thin Filament Time Training Validation Work X ray diffraction analysis base beamline computerized tools connectin data reduction data tools deep learning detector electron density experimental study improved indexing millisecond muscular structure muscular system myosin-binding protein C nebulin novel novel strategies pre-clinical predictive tools programs simulation software development tool two-dimensional user-friendly

项目摘要

Synchrotron small angle X-ray fiber diffraction is the method of choice for obtaining structural and physiological information in the same experiment from active muscle. Experimental questions addressed range from basic biophysical questions regarding mechanisms of force production and regulation to increasingly pre-clinical questions relating structure to functional phenotype in animal models for cardiomyopathies and skeletal muscle disease as well as human muscle biopsies. Critical barriers to progress, however, has been the lack of robust, user-friendly tools for data reduction and computational tools for modeling diffraction patterns that can be used as an aid to interpret the data. In Aim 1 we propose to further develop the MuscleX software package as a highly automated data-reduction suite for small-angle fiber diffraction patterns from striated muscle. We will use artificial intelligence (AI) approaches to greatly increase efficiency, reduce influence of operator bias and improve reproducibility. New functionality will include global diffuse background subtraction using “deep learning”, the ability to analyze multiple superimposed diffraction patterns, autoindexing and automatic integration of diffraction peaks and unsampled layer lines. Robustness and reproducibility of code will be improved with rigorous testing and validation procedures. In Aim 2 we propose to develop a new tool, MUSICO-X for predicting two-dimensional X-ray diffraction patterns from striated muscle. MUSICO-X will be a new extension module for the multi-scale simulation package MUSICO that predicts small-angle X-ray fiber diffraction patterns simultaneously with the physiological data as a novel “forward problem” approach to extracting maximal information from static and dynamic time resolved X-ray fiber diffraction experiments on striated muscle. This new module will assign electron densities to components of the sarcomere using predicted molecular positions from MUSICO to predict simulated diffraction patterns that are tested and refined against representative X-ray diffraction and physiological data sets. These proposed software developments are broadly applicable to all muscle systems without a specific disease focus, and would not be fundable through usual mechanisms at NIAMS or HLBI. The availability of robust, user friendly data reduction code will increase the efficiency and reproducibility data from muscle fiber diffraction experiments on muscle. The proposed new simulation tool, encompassing both the structure and function of muscle, will provide a potent hypothesis generation and testing tool that can greatly increase the value of past, present, and future X-ray diffraction experiments on muscle.

同步辐射小角X射线光纤衍射是获得结构和生理结构的首选方法同一实验中的信息来自活跃的肌肉。解决的实验问题范围从基本问题到关于力量产生和调节机制的生物物理问题越来越多地出现在临床前心肌病和骨骼肌动物模型中与结构和功能表型相关的问题疾病以及人类肌肉活组织检查。然而，取得进展的关键障碍是缺乏强有力的、用于数据简化的用户友好工具和用于对可使用的衍射图建模的计算工具作为解释数据的辅助工具。在目标1中，我们建议将MuscleX软件包进一步开发为来自横纹肌的小角度光纤衍射图的高度自动化数据简化套件。我们会使用人工智能(AI)方法大幅提高效率，减少操作员偏见的影响，并提高再现性。新的功能将包括全局漫反射背景减影学习“，能够分析多个重叠的衍射图，自动索引和自动衍射峰和未采样层线的积分。代码的健壮性和可重复性将是通过严格的测试和验证程序进行改进。在目标2中，我们建议开发一种新工具， Musico-X用于预测横纹肌的二维X射线衍射图。Musico-X将成为预测小角X射线纤维的多尺度模拟程序包Musico的新扩展模块将衍射图与生理数据同时作为一种新的“正问题”方法从静态和动态时间分辨X射线纤维衍射实验中提取最大信息横纹肌。这个新的模块将分配电子密度给肌节的成分，使用预测来自Musico的分子位置，以预测经过测试和改进的模拟衍射图对照具有代表性的X射线衍射和生理数据集。这些建议的软件开发广泛适用于所有没有特定疾病焦点的肌肉系统，将不会获得资金通过NIAMS或HLBI的常规机制。健壮、用户友好的数据缩减代码的可用性将提高肌肉纤维衍射实验的效率和重复性。这个拟议的新模拟工具，包括肌肉的结构和功能，将提供强大的假设生成和测试工具，可以极大地提高过去、现在和未来的X射线价值肌肉的衍射实验。