Meeting 4.29.11

Tasks for over the weekend:

Erkin - finish survival anaylsis, create and describe the survival plots and the methods to create them. For the discussion: provide some dialouge on why our survival plots look as they do.

Emily - Write some stuff in the discussion about the other way we originally did the experiment and why this was non-functional. Talk about drift.

April - do that flow chart of our experimental design. Add into discussion about the same things Emily was going to do. Format references.

All- read all this crap so we're not redundant/stupid/illiterate. Make sure to add your references.

Meet Tuesday to talk presentation.

Go team!

Meeting 4.21.11

Things we need to do by next Thursday:

Literature review for intro/motivations of paper and general methods for paper.

GLM and Survival Analyses: So, our data is basically the inverse of the example they gave in the R/Dalgard texts. GLM should help us break down the relationships between our variables. Write up methods and results for those.

Figures

Assignments:

GLM: Emily

Survival: Erkin

Lit Review: Me

Due Date:

Thursday, Meet in class on Tuesday to decide if anyone is over burdened and needs to shift some responsibility.

New plot! (with 99% killed instead of 95%)

So, the results change dramatically when we kill off 99% of individuals instead of 95% like we did before. (Killing off 99% leaves only 100 individuals out of the population of 10000). The discrepancy with the earlier results requires further investigation.

Questions about questions.

New plot! with some surprising results!
What question do we want to ask?


Our newest runs!


Depending on our questions, we could apply different tests.
QUESTIONS 1.
Does overall treatment vs. control make a difference (T vs C)?
Does overall how often we kill things off make a difference (A vs B)?
Is there an interaction in how these things apply?

For these three maybe General linear model in the binomial case, can be applied to our data?

QUESTIONS 2.
Is TA different from TB?
Is CA different from CB?
We can use fishers exact test, on TA vs TB independent of CA vs CB.
Fisher's exact test results:
TAvsTB: p=5.37 x 10^-9
CAvsCB: p=.48
CBvsTB: p=0.32
CAvsTA: p=1.04x10^-9
What is the impact of this repeated testing on doing this this way?

Ugh

Hey Charles, Ian and Art, we're having issues maybe.

So, we did some runs of two treatments. The first ("TA" on the graph) kills 95% of the population and changes the environment every 500 generations. The second ("TB" on the graph) kills 95% of the population and changes the environment every 5,000 generations. In the events file, we have 3 functions rewarded and 4 zeroed out. Equals is not rewarded during this initial phase. Then, we zero out all the functions except equals. The results are at the left.

We find that in the tasks files, runs that evolved equals before we zero out all the functions are the ones who get equals widely in the population. The populations who don't basically lose most functions. We think the issue here is that in populations that have equals prior to the other functions being zeroed, it spreads as a high reward function. In populations that didn't, they just lost functionality as there was no selection to keep it. Emily had a good analogy - it's like putting a mouse in water. If it already has gills, it'll do awesome. If not, well...

Going forward, we were thinking we should keep the same environment file from the last run before rewarding equals, and then just start rewarding equals. A total of 4 functions will then be rewarded. That way, there will still be selection to keep functionality. And then we can compare those results to these results. We just don't think that the way this is set up now is biologically realistic.

What do you all think?