Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets

Daniel Plaugher, David Murrugarra

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs), as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean network (BN) model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network (GRN) from a diseased state to a healthy state. To do so, we implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. We identified target genes and molecules, such that the application of their control drives the GRN to the desired state by suppression (or expression) and disruption of specific signaling pathways that may eventually lead to the eradication of the cancer cells. After applying well-studied control methods such as stable motifs, feedback vertex sets, and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Yet, we were able to gain more insight about the performance of each process and the overlap of targets discovered. Nearly every control set contains cytokines, KRas, and HER2/neu, which suggests they are key players in the system’s dynamics. To that end, this model can be used to produce further insight into the complex biological system of pancreatic cancer with hopes of finding new potential targets.

Original languageEnglish
Article number115
JournalBulletin of Mathematical Biology
Issue number11
StatePublished - Nov 2021

Bibliographical note

Funding Information:
DM thanks Boris Aguilar for the discussions and suggestions during the initial stage of this project. DM thanks the Southeastern Conference (SEC) Faculty Travel Grant for their partial support. The authors thank the anonymous reviewers for their comments during the review process.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Society for Mathematical Biology.


  • Boolean networks
  • Math modeling
  • Pancreatic cancer
  • Phenotype control

ASJC Scopus subject areas

  • Neuroscience (all)
  • Immunology
  • Mathematics (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Environmental Science (all)
  • Pharmacology
  • Agricultural and Biological Sciences (all)
  • Computational Theory and Mathematics


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