Martina Daelli
April 5th, 2017
Midterm Paper – Behavioral Description
1) Describe what the code in your section is supposed to do. What are the agents? What do the
agents do? What do the agents sense? How do the agents respond? This should describe their
behavior - not how the code makes this happen.
The behavior of my code is focuses on conformational changes that happen upon collision
between agents. The agents that are involved in the behavior are four: Cancer cells agent, T-cells
(lymphocytes) agent, Cancer signals agent, and Stamp Heart agent.
The cancer cells agent procreates daughter cancer cells from one original cell situated in a
portion of the terrain denominated as “lungs”. Cancer cells grow and keep procreating simulating
how real cancer cells grow into foci, invading the organ on which they are reproducing. One special
feature of the cancer cells agent is that after a determined amount of time some of the cancer
cells that make up the focus have a chance of becoming metastatic cells, which can be
differentiated from the regular cancer cells because the regular cancer cells are colored yellow
meanwhile the metastatic cells are colored blue. This agent can sense the color of the terrain and
depending on such color it will either remain blue and thus wondering around or if the color of
the terrain is red then the metastatic cell will have a probability of settling down on the colored
terrain, thus turning yellow. Regular cancer cells are yellow and the property of settling down and
stop moving, whether metastatic cells are blue and have the property of roaming around the
terrain.
The Stamp Heart agent is an agent that stamps the terrain just once, at the beginning of
the simulation. This agent does not sense anybody, it is just there to simulate the heart. Cancer
cells agent respond to the heart agent by attaching to it through a change in color from blue to
yellow.
The Cancer Signals agent is an agent that comes out of the cancer cells focus with the
purpose of potentially mutating T-cells upon contact, deactivating them. Cancer signals do not
sense anybody but on collision with the agent T-cells they have a chance of deactivating them.
Once they hit and deactivate a T-cell, cancer signals die and get deleted as a response to the
collision with the lymphocytes.
The T-cells agent represent the lymphocytes, the white-blood cells hence the body’s army
against the cancer cells. When T-cells are white they are fully functioning and they have a chance
of killing the cancer cells present in the focus, meanwhile if the T-cells get hit by the cancer signals
they have a chance of turning orange and change their conformation, which deactivates them
meaning that they will no longer able to kill cancer cells.
2) Describe how your code implements the behavior you described in (1). Be sure to include
screenshots of the code with enough context so that I can understand how it works. Describe all
traits and what they represent.
This screenshot above represents the behavior of T-cells. In the first collision box it is possible to
see that cancer cells get deleted on the turquoise terrain and on the gray terrain (which is the
lung) with a different chance than on the red terrain. This was created this way because on the
red terrain (heart) metastatic cancer cells will be growing, and studied showed that killing
metastatic cancer cells is much harder due to more mutations that have been accumulated by the
cells. On the second collision box it is possible to see how T-cells get affected by the cancer signals.
Upon collision with cancer signals T-cells have a chance of changing color and size and then the
cancer signal gets deleted. It is important to notice that T-cells are able to kill cancer cells only
when they are white in color.
This
screenshot
shows
metastatic
cancer
cells
behavior in the forever mode.
Once a cancer cell becomes
metastatic it undergoes a
change in color from yellow to
blue and it detaches from the
focus of cells. With that color
change, it acquires the ability
to start roaming around the
terrain and if it finds itself on
the red terrain (heart) it has a
chance of turning yellow, thus
settling down because yellow cells don’t have the ability to move.
The above screenshot is more complicated and it explains the behavior of the other cancer cells.
It is possible to notice that as stated before, cancer cells grow very close to one another at a
controlled but steady rate and they are not able to move after spawning. After a certain amount
of days, cancer cells have a chance of turning blue, thus metastatic and they will be following the
metastatic rules given by the previous code, which are dictated by the change in color. Metastatic
cells will also have a half-life, such half-life was set because not all cells that become metastatic
end up infecting another organ, therefore after a certain amount of time roaming around they will
either die by the hands of the T-cells or they will just die because of their half-life. The bottom part
of the code shows the implement of the half-life and how that is set up. Every tick of the clock the
half-life gets diminished by a number of 2 and once the half-life number reaches 2 or below the
cell will die.
3) How do you know that your code “works”? Give two examples of how you tested it, what should
have happened if the code was working, and what actually happened. These tests must involve
changes to the simulation. In your case, these could involve changing the starting numbers of
organisms or changing their behavior when they interact with other organisms, etc. and observing
the effects of these on the numbers and behavior of each type of organism. You could also remove
some key element of the simulation, then predict and observe the results. Be sure to include graphs
if appropriate.
I have made various changes to my code before getting it to work. The hardest thing was
to get the whole metastatic effect into play as well as an appropriate half-life calibration, which is
crucial to my simulation of cancer development. When I was trying to get the metastatic cells to
settle back down when sensing the red terrain what I did at first was I had tried to simply set their
forward movement block to zero if the terrain color was equal to red, which in theory should have
had them stop moving, hence settling down, but that did not work. The forward 0 setting did not
accomplish the goal of stopping the cells and allowing them to start a tumor in a different organ,
in fact the cells kept moving, following the instructions given to the blue cells. After that I came up
with the idea to just tell the blue cells to turn their color back to yellow once they sensed the red
terrain, which made it work since I had already set the rules for the yellow cells not to move but
to just spawn next to each other. This last approach worked.
My other struggle was with the metastatic cells half-life. I wanted to allow the cells to roam
around the terrain for a decent amount of time to allow them to pass over the red terrain couple
of times before actually dying themselves. This procedure involved a lot of attempts. First of all, I
started off setting the initial half-life to 100, but that did not give the metastatic cells time to
detach from the original tumor that they were killed off immediately. Then I moved up the halflife amount to 1000 but that was way too much because it took 10 days on the simulation clock to
kill them off, which was too much time and it resulted in an extreme overpopulation of the
metastatic cells, which is not a real simulation of events. From 1000 initial half-life amount I kept
trying by subtracting 100, meaning I had a test run with 900 half-life, then 800 etc.. but none of
them allowed for the cells to roam around as well as avoiding metastatic overpopulation until I
reached 200 half-life count, which works and allows for a generous time around the terrain as well
as an efficient prevention from excessive overpopulation.
4) Give one example of a hypothesis you can test with your simulation and how you’d test it.
With my simulation, I want to test immune activation therapy. This therapy focuses on the
re-activation of the body’s immune system to boost it to attack and kill cancer. I want to test if
through the implementation of a drug which re-activates the mutated T cells, the cancerous mass
will shrink more evidently due to cancer cells death. The way I want to test this is by creating a
new Drug Agent, which can change T-cells conformation and color to their original state upon
collision with the mutated orange T-cells. By bringing back T-cells to their original state, the drug
would re-activate the mutated T-cells that were no longer able to kill cancer cells, allowing them
to tackle the cancer. Through the use of the drug the number of active T-cells should increase,
thus increasing the velocity at which cancer gets killed and hopefully the simulation will show that
through the presence of many active T-cells the cancer cannot duplicate and grow faster than the
rate at which T-cells kill it.