Bayesian estimation of muscle mechanisms and therapeutic targets using variational autoencoders

Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially preventing such damage. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. This repository contains the training and testing datasets we used in the associated research article. 

Methods
We generated this dataset by using our spatially explicit muscle model, available at https://github.com/travistune3/multifil_five_state. In this model, the myosin containing thick filaments and actin containing thin filaments are composed of a series of springs, and crossbridge formation and state changes are tracked for each myosin-actin pair individually. Crossbrdige kinetics of each head can be modified. We randomly generated rate factors over a log uniform scale from 10^-1 to 10^2, and multiplied those rate factors by the 'default' rates. We did this for 9 total rates from both the myosin motors and actin binding sites. We then took the rate factor combination and simulated the twitch which resulted from that rate 50 times and averaged for a final twitch. We then did this 10^6 times to form our training dataset. 

We record here the rate factors, 'default' rate, and the training and testing split used.