  The focus of pancreatic cancer research has been shifted from pancreatic
cancer cells towards their microenvironment, involving pancreatic stellate
cells that interact with cancer cells and influence tumor progression. To
quantitatively understand the pancreatic cancer microenvironment, we construct
a computational model for intracellular signaling networks of cancer cells and
stellate cells as well as their intercellular communication. We extend the
rule-based BioNetGen language to depict intra- and inter-cellular dynamics
using discrete and continuous variables respectively. Our framework also
enables a statistical model checking procedure for analyzing the system
behavior in response to various perturbations. The results demonstrate the
predictive power of our model by identifying important system properties that
are consistent with existing experimental observations. We also obtain
interesting insights into the development of novel therapeutic strategies for
pancreatic cancer.
