# Analysis Tools for Fiber Diffraction of Muscle

> **NIH NIH R01** · ILLINOIS INSTITUTE OF TECHNOLOGY · 2022 · $395,635

## Abstract

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.

## Key facts

- **NIH application ID:** 10344800
- **Project number:** 1R01GM144555-01
- **Recipient organization:** ILLINOIS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** THOMAS C IRVING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $395,635
- **Award type:** 1
- **Project period:** 2022-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10344800

## Citation

> US National Institutes of Health, RePORTER application 10344800, Analysis Tools for Fiber Diffraction of Muscle (1R01GM144555-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10344800. Licensed CC0.

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