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Agent-Based Exploration of Plant-Pollinator Mutualism
From as early as 1869, apiarists have reported a set of symptoms in which colonies lose many adult worker bees leaving behind large stores of food, brood, and even the queen. Colony Collapse Disorder, as described above, continued at a steady incidence rate of ~17-20% in the 1990s and early 2000s. The rate of CCD started to increase, however, in November of 2006 to between 30% and 90% (an admittedly large range).
Figure 1: A European honey bee Apis mellifera extracts nectar from an Aster flower using its proboscis. Tiny hairs covering the bee's body maintain a slight electrostatic charge, causing pollen from the flower's anthers to stick to the bee, allowing for pollination when the bee moves on to another flower. Image released into the public domain by John Severns.
Bees are an important component in the pollination of plants, particularly in modern agriculture where bees are known to pollinate over 120 different species of crop. Given that pollinators, such as bees, are known to develop mutualistic relationships with particular species of plants, Matthew Taylor, Andrew Patt, and I set out to create an agent-based model to explore how obligate pollination affects the dynamics of plant competition.
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Types of time for simulations
For the last few weeks a couple of colleagues and I have been modeling competition in pollinating plants under ecology professor Dr. Gregg Hartvigsen. In our particular research, a 2D spatial simulation consisiting of agents simulate plant and bee behavior. At face value, the model looks similar to cellular automata, but in this case the rules are slightly more complex.
Figure 1: An early run of our model's first version. This is a typical domination case, where one species out competes another. This version, however, is flawed in the evaluation of discrete time, in which some cells have reproduction bias.
After we started testing our model, we realized a massive mistake: we biased some plants over others when we handled time. This led to a question of if we should adapt our model to use continuous time or discrete time and what those two approaches would entail.
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How challenging are Geneseo's classes?
This week fellow student and fellow data analysis enthusiast Herb Susmann released student-reported SOFI data on courses at SUNY Geneseo, welcoming people to see what interesting relationships -- or lack thereof -- they could find in the data.
To that end, I downloaded the data, fired up R, and decided to compare how challenging students rated their classes. The individual course data was too narrow a data set, so I examined the data for classes in the natural sciences, the social sciences, and the fine arts.
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The enzyme kinetics showdown
Last week, I was asked to review a new case study for W.H. Freeman for their 7th edition of Lenninger's Biochemistry. The study involved using enzyme kinetics to discover the identiy of a poisoner using an unnamed inhibitor. Alas, I had a difficult time getting the correct maximum enzyme velocity and Michaelis constant.
Initially, I did the usual non-linear regression of the kinetics plot in R using
nls, but that wasn't right. Next I tried a Hanes-Woolf plot because of it's relative accuracy at finding constants. As a last chance effort I made a Lineweaver-Burk plot which had the "correct" values.Educationally, the Lineweaver-Burk plot is used as a mostly accurate determination of the kinetics constants while being easy to both construct and read. However, the differences in computed constants between a Lineweaver-Burk, Hanes-Woolf, and basic non-linear regression seems non-trivial. Just how different are they?
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