PhysiCell demo: immune cells attacking a heterogeneous tumor
Ahmadreza Ghaffarizadeh
Randy Heiland
Samuel Friedman
Shannon Mumenthaler
Paul Macklin
10.6084/m9.figshare.5717887.v1
https://figshare.com/articles/media/PhysiCell_demo_immune_cells_attacking_a_heterogeneous_tumor/5717887
<p>This is Video S8 in Ghaffarizadeh
et al. (2018). A higher-resolution (4K) video can be streamed at <a href="https://www.youtube.com/watch?v=nJ2urSm4ilU">https://www.youtube.com/watch?v=nJ2urSm4ilU</a>
</p><p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p><p>This
is a PhysiCell demo of immune cells (red) attacking a 3-D heterogeneous tumor,
using a basic biophysical model of an adaptive immune response to a tumor. In
the simulation:</p><p>1)
Cancer cells each have an individual expression of a mutant oncoprotein, which
drives proliferation. Yellow cells divide faster (lots of oncoprotein) than
blue ones (very little oncoprotein).</p><p>2)
If the tumor gets too big, it outstrips the nutrient supply and a necrotic core
(dead center) forms.</p><p>3)
As a simple model, tumor cells release an immunostimulatory factor that
diffuses outward. Cells are assumed to have immunogenicity proportional to the
mutant oncoprotein (e.g., by altering MHC with mutant peptides).</p><p>4)
At 14 days, we introduce 7500 immune cells (red) which perform a biased random
walk towards the immunostimulatory factor.</p><p>5)
Whenever an immune cell touches another cell, it:</p><p>..
a) adheres to the cell</p><p>..
b) checks for immunogenicity</p><p>..
c) induces apoptosis in the tumor cell at a rate proportional to
immunogenicity)</p><p>..
d) detaches either after a random time or after inducing apoptosis in the tumor
cell</p><p>6)
Immune cells break away from newly apoptotic cells (cyan) and continue to seek
more targets.</p><p>The
simulation took about 2 days on a quad-core desktop (i7-4770k), including time
spent on saving simulation data once every 3 simulated minutes. Simulations
with less frequent output are substantially faster.</p><p><b>Legend:</b></p><p><u>Blue
cells</u>: tumor cells with oncoprotein < 0.5</p><p><u>Yellow
cells</u>: tumor cells with oncoprotein > 1.5</p><p><u>In
between</u>: tumor cells with 0.5 < oncoprotein < 1.5</p><p>(yellow
is greater)</p><p><u>Dark
dots</u>: cell nuclei</p><p><u>Cyan
cells</u>: apoptotic tumor cells</p><p><u>Brown
cells</u>: necrotic tumor cells and debris</p><p><u>Red
cells</u>: attacking immune cells.</p><p>This
work is based on PhysiCell, an open source 3-D modeling package for
multicellular biology at <a href="http://PhysiCell.MathCancer.org">http://PhysiCell.MathCancer.org</a>.</p><p><b>Method: </b>Demonstration of PhysiCell, an agent-based,
lattice-free model. Cell velocities determined by balance of adhesive,
repulsive, and motile forces. Each cell has a phenotypic state governed by
stochastic processes derived from nonhomogeneous Poisson processes.</p><p><b>Software source: </b>PhysiCell is available as open
source at <a href="http://PhysiCell.MathCancer.org">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>. </p>
2017-12-20 05:37:49
PhysiCell
immuno-oncology
mathematical biology
mathematical oncology
open source
biomathematics
simulation
heterogeneity
cancer
tumor
agent-based models
Computational Biology
Cancer
Tumour Immunology
Immunology
Open Software
Simulation and Modelling