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In this App, we developed a dynamic interaction model which contains two systems: an infected system of cells and a off-site immune system that respond to the infection. We observe that immune response changes depending on the intensity of the infection cells. Our aim is to investigate how the immune system modulate its response according to variation in the infection.
There are 3 types of cells: infection cell/invader cell, scout cell and immune cell. We used Physicell and BioFVM as our tool to model the binary systems.
Invader cell: Form clusters and secrete chemoattractant
- Multiply faster when in cluster
- Death rate also affected by pressure
- Actively seek higher chemoattractant zones
Scout cell: Track and identify the invader population
- Seek the high chemoattractant zones to find invader cells
- On contact, learn the marker (a random number) and leave for the offsite system
- Train immune cells to kill the invader
Immune cell: Track and kill the invader cells
- Move and search for contact and induce apoptotic death if the cell is invader type
- Death rate also affected by pressure to control population
- Avoid contact with scouts
Off-site ODE model:
Key features in this model:
Birth and death:The cell birth rate is a crucial parameter that regulates how soon a cell will divide into two identical daughter cells. This can dramatically affect the cell populations positively or negatively. In our case, this parameter is changed based on the gradient of the Chemoattractant. For ex., the invader cells divide quickly when in group (implying higher levels of the chemoattractant).
Secretion and uptake: Our model relies heavily on the secretion and uptake process to indirectly affect the chemical substrates
throughout the simulation domain. Evidently, the changes in the gradients is what guides the cells to move with a biased direction in the domain.
Biased migration: In our model the cells perform biased migration. This means that the cells are moving with a pre-defined goal to reach specific locations in the domain. For example, an invader cell wants to find other invader cells so that they can cluster together and multiply fast, this is made possible by the biased migration.
Mechanics: Our model uses the contact-based communication to detect and identify invader cells. This is implemented in the form of cell rules that govern the motion of scout and immune cells.
This model was created to highlight the interplay of a distributed immune systems with other organs in the body. For example, the offsite system here can be thought of as a lymph node which filters the cells to look for scout cells and accordingly generate trained immune cells to seek and kill the invaders in the system. Evidently, much fine tuning in terms of the birth, death rates etc. can be done to further refine this system and there is scope for improvement. Our aim was to study how the two systems would play with each other during the simulation and we got some very interesting results with such a simple setup.
This software is powered by PhysiCell, a powerful simulation tool that combines multi-substrate diffusive transport and off-lattice cell models. PhysiCell is BSD-licensed, and available at:
- GitHub releases: https://github.com/MathCancer/PhysiCell/releases
- SourceForge downloads: https://sourceforge.net/projects/physicell/
It is a C++, cross-platform code with minimal software dependencies. It has been tested and deployed in Linux, BSD, OSX, Windows, and other environments, using the standard g++ compiler.
Randy Heiland, Research Associate, Intelligent Systems Engineering, Indiana University.
Paul Macklin, Ph.D. , Associate Professor, Intelligent Systems Engineering, Indiana University
We appreciate both Randy and Paul's support in debug source code and test the GUI
 Ghaffarizadeh A, Heiland R, Friedman SH, Mumenthaler SM, Macklin P (2018) PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput Biol 14(2): e1005991. https://doi.org/10.1371/journal.pcbi.1005991
 Ghaffarizadeh A, Friedman SH, Macklin P (2016) BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32(8):1256-8. https://doi.org/10.1093/bioinformatics/btv730
 Randy Heiland, Daniel Mishler, Tyler Zhang, Eric Bower, Paul Macklin (2019) xml2jupyter: Mapping parameters between XML and Jupyter widgets. DOI: https://doi.org/10.1101/601211
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