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.