A mathematical model for pancreatic cancer during intraepithelial neoplasia
Data files
Oct 15, 2024 version files 3.65 MB
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full_GRN.ipynb
2.29 MB
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README.md
1.34 KB
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Voronoi_cells.ipynb
1.36 MB
Abstract
Cancer is the result of complex interactions of intrinsic and extrinsic cell processes, which promote sustained proliferation, resistance to apoptosis, reprogramming, and reorganization. The evolution of any type of cancer emerges from the role of the microenvironmental conditions and the impact of some molecular complexes on certain signalling pathways. The understanding of the early onset of cancer requires a multiscale analysis of the cellular microenvironment. In this paper, we analyse a quantitative multiscale model of pancreatic adenocarcinoma by modelling the cellular microenvironment through elastic cell interactions and their intercellular communication mechanisms, such as growth factors and cytokines. We focus on the low-grade dysplasia (PanIN 1) and moderate dysplasia (PanIN 2) stages of pancreatic adenocarcinoma. To this end, we propose a gene regulatory network associated with the processes of proliferation and apoptosis of pancreatic cells and its kinetics in terms of delayed differential equations to mimic cell development. Likewise, we couple the cell cycle with the spatial distribution of cells and the transport of growth factors to show that the adenocarcinoma evolution is triggered by inflammatory processes. We show that the oncogene RAS may be an important target for developing anti-inflammatory strategies that limit the emergence of more aggressive adenocarcinomas.
https://zenodo.org/doi/10.5281/zenodo.13787992
The data files contain the numerical simulation programs used to model the delay equations of the genetic regulatory network, the interaction of cytokinins in a Lotka-Volterra model, and the evolution of cellular phenotypes in a two-dimensional array of voronoi-type cells.
Description of the data
All programs can run independently using GitHub’s open in Colab. They can also be executed using classic Python tools.
The programs can run independently using Jupyter Notebooks with classic Python tools. Also they can be executed using our GitHub’s repository (https://github.com/joshbrx/biomath_model.git) and run them in Jupyter Notebooks with Open Colab application.
Here, the data scripts contain the evolution of cells in the reduced genetic network. In the script full_GRN.ipynb you will find the code to simulate healthy and cancer cells with the reduced genetic network. The script Vornoi_cell.ipynb simulates the evolution of acinar cells with Voronoi diagrams. Also, the script is used it to determine the cell prototypes and inflammatory process during early intraepithelial neoplasia.
We considered a genetic regulatory network in the development of pancreatic cancer and modified it to observe only the cellular phenotypes that give rise to proliferation and apoptosis in healthy and cancerous cells. We proposed a dynamic model with differential equations with delay to show that the accumulation of mutations generates regressions in the genetic network of healthy cells causing the transition to cells with cancer. The delay in the actions of the Ras gene is considered the main trigger of neoplasia and inflammation in tissues. We built a spatial model, with polynomial cells, to show the correlation between inflammation, the regulatory network and the proliferation and apoptosis rates of cells in the tissue.