PDF(Pubmed)
Abstract：
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. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with both control and the cardiac Troponin C (cTnC) I61Q variant in mouse trabeculae and model parameters that recover the abnormal I61Q cTnC twitches.
CONCLUSIONS: Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.
CONCLUSIONS: Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.
摘要：
心肌病,通常由编码肌肉蛋白的基因突变引起，传统上通过对心脏进行表型分析并解决不可逆损伤后的症状。随着基因分型的进步，早期诊断是可能的，有可能防止这种损害。然而,肌肉的复杂结构及其无数的蛋白质使得治疗预测具有挑战性。在这里，我们使用空间明确的半肌节肌肉模型来解决估计小鼠肌肉突变的治疗靶标的问题。我们在模型中选择了9个与小分子和引起心肌病的突变相关的速率参数。然后，我们随机改变这些速率参数，并为每个组合模拟等距抽搐，以生成大型训练数据集。我们使用这个数据集来训练条件变分自动编码器(CVAE)，贝叶斯参数估计中使用的一种技术。给定模拟或实验的等距抽搐，然后，该机器学习模型能够预测最有可能产生该结果的速率参数集。然后，我们预测与小鼠小梁中的对照和心脏肌钙蛋白C（cTnC）I61Q变体相关的一组速率参数，以及恢复异常61QcTnC抽搐的模型参数。
