Simulating Life

This week, I implemented a second condition that allows sick/sterile agents to rarely become fertile. This runs on a seasonal system, with the second condition only becoming apparent during the summer months. For next week, I plan to work on my paper; I would like to do Topic A. I think I have completed all the goals I set out to complete when I began this project, and there is nothing more for me to work on or add.

The graph above shows the results I obtained when I ran my simulation ranging from 1-10 clinics in the population. I noticed that the number of females that prevented the transmission of HIV to the fetus increased quite significantly as the number of clinics increased. I expected the number of females that didn’t make it to the clinic in time and wasn’t able to prevent virus transmission to decrease much more drastically as the number of clinics increased. It appears to me that I would need far more than 10 clinics to see a drastic and decrease in those numbers. This is my data thus far 🙂

I think I have everything all set. My plan for next week is to take data and note the average time when any of the preys first gets wiped out completely in the presence of four lands and then two lands to help create stability in the world.

This week we plan to fix the T cells so that when they collide with an infected cell there is a chance that more T cells will be produced. This way, when the virus comes around the second time the immune response will be better prepared (because there would be more T cells).

Jen & Rachel

Today we added the data boxes for the avg number of cells and the total cell count. This provides us with accurate numbers for when we change the probabilities throughout our simulation. Instead of eyeballing the numbers, we can now clearly see the affects of the changes we make. We will continue to gather information for our excel sheets so that next week we can focus on our papers.

Coding wise, I think I am actually good. Howeverrrrrr I am debating if I should include oxygen on my graph. If I do, it makes for quite a mess graph. The picture I included is just the start of the simulation but since the oxygen is going to be constantly increasing and everything else won’t be increasing as quickly..makes it quite difficult to read the graph towards when I want to take data. Your thoughts? Thanks! 

I have been running sample trials all week and examining data, and I have one problem:

The Crested toads are still going extinct, even with intervention.

I am now working to bring the numbers to a more realistic output for lifespan and breeding age.  If this works it will over a better demonstration of what to expect with human intervention to save this endagered species.  Because the sad truth is, the Crested Toad population has seen some bounce back in real life with human aid, but given the damage that was done by the invasive Cane Toad population the species MAY still become extinct.

An idea I may incorporate next week is adding pheromone traits to the frogs, and possibly having humans reintroduce Crested Toad eggs from captivity to this wild population, further demonstrating human aid.  If I do the reintroduction I will need a remix, because I want to run both simulations at the open house to show the different levels of human intervention

The Cane Toads are being killed slightly more quickly then I would like, but that is an easy repair in the numbers.

We were able to gather a lot of data from our simulation, using different variables to test it.  We put this data into graphs and saw a pattern, so we made a hypothesis for how changing the reproduction rate of the cancer cells will affect the spread of the tumor.  We are going to run more tests to see what happens

I finished taking all my data for my simulation. My graph looks a lot better from my previous graph and my R^2 is better than my previous graph. I will begin to start doing my final paper and my handout next week. 

I have taken data  and created a graph where the x axis is the number of pigs and the y axis is the number of humans infected with H1N1. The R squared value for the graph is 0.56.  I also went to the humans tab and made it so that if the humans come into contact with the blue terrain that represents water, they change their heading towards the opposite direction so that humans can’t walk into the water.