Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets View Full Text


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Article Info

DATE

2021-10-11

AUTHORS

Daniel Plaugher, David Murrugarra

ABSTRACT

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. More... »

PAGES

115

References to SciGraph publications

  • 2013-06-11. Applying local search to the feedback vertex set problem in JOURNAL OF HEURISTICS
  • 1999. Feedback Set Problems in HANDBOOK OF COMBINATORIAL OPTIMIZATION
  • 2016-09-23. Identification of control targets in Boolean molecular network models via computational algebra in BMC SYSTEMS BIOLOGY
  • 2012-06-06. Modeling stochasticity and variability in gene regulatory networks in EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY
  • 2013-07-02. Dynamics and Control at Feedback Vertex Sets. I: Informative and Determining Nodes in Regulatory Networks in JOURNAL OF DYNAMICS AND DIFFERENTIAL EQUATIONS
  • 2014-06-26. Steady state analysis of Boolean molecular network models via model reduction and computational algebra in BMC BIOINFORMATICS
  • 2016-09-04. Formal Modeling and Analysis of Pancreatic Cancer Microenvironment in COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY
  • 1972. Reducibility among Combinatorial Problems in COMPLEXITY OF COMPUTER COMPUTATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11538-021-00937-w

    DOI

    http://dx.doi.org/10.1007/s11538-021-00937-w

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1141789381

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34633559


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