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 introducing earlier treatment. 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 nine 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 data set. We used this data set to train a conditional variational autoencoder, 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 that are most likely to yield that result. We then predict the set of rate parameters associated with twitches from control mice with the cardiac troponin C (cTnC) I61Q variant and control twitches treated with the myosin activator Danicamtiv, as well as model parameters that recover the abnormal I61Q cTnC twitches.

中文翻译：

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使用变分自动编码器对肌肉机制和治疗靶点进行贝叶斯估计


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来治疗。随着基因分型的进步，现在可以进行早期诊断，从而有可能引入早期治疗。然而，肌肉及其无数蛋白质的复杂结构使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模型来处理估计小鼠肌肉突变的治疗靶点的问题。我们在模型中选择了与小分子和导致心肌病的突变相关的 9 个速率参数。然后，我们随机改变这些速率参数，并模拟每个组合的等距抽搐，以生成一个大型训练数据集。我们用这个数据集来训练一个条件变分自动编码器，这是一种用于贝叶斯参数估计的技术。给定模拟或实验性等距抽搐，此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，我们预测了具有心肌肌钙蛋白 C （cTnC） I61Q 变体的对照小鼠和用肌球蛋白激活剂 Danicamtiv 治疗的对照小鼠的抽搐相关的速率参数集，以及恢复异常 I61Q cTnC 抽搐的模型参数。