从表观基因组预测基因表达的动力系统模型
A dynamical system model for predicting gene expression from the epigenome
摘要：
基因调控是一个重要的基本生物学过程。基因表达的调节通过多种方法进行，包括表观遗传过程（例如，DNA甲基化）。了解表观遗传变化在基因表达中的作用是分子生物学的一个基本问题。从表观遗传学数据预测基因表达值具有巨大的研究和临床潜力。尽管研究活跃，但迄今为止的研究集中在使用统计模型从甲基化数据预测基因表达。相比之下，动态系统可用于生成模型以使用表观遗传数据和基因调控网络（GRN）来预测基因表达，所述GRN也可用作机制假说。在这里，我们提出了一种新的随机动力系统模型，预测基因表达水平的甲基化数据的基因在一个给定的GRN。我们使用真实的患者数据和从强大的参考源创建的GRN提供了一个模型的评估。用于数据集准备、模型参数拟合和预测生成以及报告的软件可在\verb上获得|https://github.com/kordk/stoch_epi_lib|.
英文摘要：
Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigenetic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. We provide an evaluation of the model using real patient data and a GRN created from robust reference sources. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at \verb|https://github.com/kordk/stoch_epi_lib|.
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