Back in December PNAS published a paper from Oliveira et al. called “Evolutionary limits to cooperation in microbial communities” . My research interests lie right at the intersection of evolution and cooperation so I was fascinated by the idea that evolution imposes limits on cooperation. In this paper, Oliveira et al. examine the evolutionary dynamics of a community of microbes that can exchange a number of valuable secretions between different strains. This experimental setup enables the evolution of cooperation if one genotype focuses on producing one secretion and shares that secretion with a different strain while also gaining access to that other strain’s secretions. Ultimately, however, they found that cooperation only evolved under specific and limited conditions because of the fitness decrease that occurs when an individual isn’t close enough to receive secretions from another strain. Because I enjoy reading papers […]
I flip-flop between Python, R, and D3 for my data visualizations depending on what exactly I’m doing. My default, though, is definitely Python. One of the most well-established data visualization libraries in Python is Matplotlib. If you dig deep enough in it, you can find a wide variety of features beyond standard graphs. One of the less well-documented of these features is the animation library. The FuncAnimation class in particular is quite powerful, allowing you to programmatically generate the frames for your animation and compile them together. Jake VanderPlas has a great tutorial on using FuncAnimation which I’m not going to try to duplicate. Here, I’m just going to focus on a small but critical aspect of using FuncAnimation that is glossed over elsewhere: blitting. Here’s how critical blitting is: My first attempted Matplotlib animation took […]
I recently read a cool paper from GECCO 2012 called “Spatial Co-Evolution: Quicker, Fitter and Less Bloated”  by Robin Harper. This paper explores some benefits of the Spatial Co-evolution in Age-Layered Planes (SCALP) algorithm, originally described by Harper in a previous paper (paywall) . In SCALP, the population lives in a three-dimensional grid of cells. Each cell is inhabited by both a solver (the “host”) and a test case (the “parasite”). Solvers try to find the right answer for each test case that is either next to or below them. They gain fitness by more closely approximating the desired answer. Test cases, on the other hand, gain fitness by being hard to get right – fitter test cases are those for which solvers have higher error. That explains the “spatial co-evolution” part of the algorithm, but what about the age-layered planes? […]
As many of us are starting the winter semester, it seemed an opportune time to discuss surviving not just the winter weather (here’s hoping Michigan’s winter isn’t as bad as last year!), but the rather mentally and emotionally trying process that is graduate school. I’m currently in my third year at MSU and am just finishing up classes, so these tips are only guaranteed to be even partially relevant for your first few years, but hopefully they continue to help me and you in our final years as well!
Happy Holidays! We hope that you’re taking at least a bit of a break from pipetting and typing sometime last week or this week. In the mean time, here’s a pretty graph to add to the flashing lights: Cheers! -Anya