Formal Modeling and Analysis of Pancreatic Cancer Microenvironment

TL;DR

This paper presents a computational modeling framework for the pancreatic cancer microenvironment, integrating intracellular signaling and intercellular communication to predict system behavior and inform therapeutic strategies.

Contribution

It introduces an extended rule-based modeling approach combining discrete and continuous variables for pancreatic cancer and stellate cell interactions.

Findings

Model accurately predicts known experimental observations.

Identifies key system properties influencing tumor progression.

Suggests potential therapeutic targets based on model analysis.

Abstract

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.