  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|.
