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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射线光纤衍射是获得结构和生理数据的首选方法