Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic 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. Dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN)  . Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. 
Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic 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. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at URL
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Dynamical
Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical
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which can also serve as a mechanistic hypothesis
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Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at URL