Analysis Tools for Fiber Diffraction of Muscle

肌肉纤维衍射分析工具

基本信息

批准号：
10344800
负责人：
THOMAS C IRVING
金额：
$ 39.56万
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/10344800
关键词：
Address Adoption Algorithms Animal Model Area Artificial Intelligence Biophysics Biopsy Cancer Grant Supplements (P30)Cardiomyopathies 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 crystallinity data reduction data tools deep learning detector electron density experimental study improved 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.

同步加速器小角度 X 射线纤维衍射是获得结构和生理学信息的首选方法来自活跃肌肉的同一实验中的信息涉及到基本的实验问题。关于力量产生和调节机制的生物物理学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结构与功能表型的相关问题然而，进展的关键障碍是缺乏强有力的、用户友好的数据缩减工具和可用于建模衍射图案的计算工具作为解释数据的辅助手段，在目标 1 中，我们建议进一步开发 MuscleX 软件包作为我们将为横纹肌的小角度纤维衍射图案提供高度自动化的数据缩减套件。使用人工智能（AI）方法大大提高效率，减少操作员偏见的影响新功能将包括使用“深度”进行全局漫反射背景扣除。学习”，能够分析多个叠加的衍射图案、自动索引和自动衍射峰和未采样层线的集成将提高代码的鲁棒性和再现性。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，用于预测横纹肌二维 X 射线衍射图案的 MUSICO-X 将是一个。用于预测小角度 X 射线光纤的多尺度仿真包 MUSICO 的新扩展模块衍射图案与生理数据同时作为一种新颖的“正向问题”方法从静态和动态时间分辨 X 射线光纤衍射实验中提取最大信息这个新模块将使用肌节的组成部分分配电子密度。从 MUSICO 预测分子位置，以预测经过测试和改进的模拟衍射图案针对代表性的 X 射线衍射和生理数据集。广泛适用于没有特定疾病重点的所有肌肉系统，并且无法获得资助通过 NIAMS 或 HLBI 的常用机制，将提供强大的、用户友好的数据缩减代码。提高肌肉肌纤维衍射实验数据的效率和可重复性。提出的新模拟工具，涵盖肌肉的结构和功能，将提供有效的假设生成和测试工具，可以大大提高过去、现在和未来 X 射线的价值肌肉衍射实验。