In Part 1, I described the motivations behind a “Freshman Interest Group” (FIG) seminar I taught last term, called “Modeling Life,” that explored how contemporary science can make sense of biology by way of physical and computational models. I also wrote about several of the topics explored in the class. Here, I’ll describe some of the assignments and projects, along with thoughts on whether the course succeeded in its aims, and whether I’ll teach it again.
Assignments
Since the course was only a one-credit, one hour per week seminar, and was focused on awareness of what can and can’t be done with models rather than actually conveying skills in modeling, I kept the assignments minimal. Many weeks involved just writing a paragraph or two. For example, following the first class’ discussion of a paper modeling waves of jostling penguins (see Part 1), students had to “Think of at least one other system besides penguins (biological or not) that would be amenable to this sort of modeling of interactions, and describe what ingredients or rules you’d put into a model of it.” Students proposed various systems of interacting agents, nearly all involving animals, people, or cars. This led to a nice discussion of, for example, the field of traffic modeling, and to Itai Cohen’s group’s simulations of “Collective dynamics in mosh pits.”
All FIGs are supposed to do something with the library, and so I came up with an assignment I’m quite fond of that explored the “demographics” of article authorships. The students picked one of two papers that we had mentioned in class:
- Golding, J. Paulsson, S. M. Zawilski, E. C. Cox, Real-Time Kinetics of Gene Activity in Individual Bacteria. Cell. 123, 1025–1036 (2005).
- M. G. I. Langille et al., Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
and then looked “forward” and “backwards” at some subset of its citations (e.g. via Web of Knowledge) and its references. The students picked at least two characteristics like:
- What departments the authors are from;
- What countries the authors are from;
- Whether the papers are about experiments, computation, or both (just determined from the abstract)
and described what they found about the collection of studies linked to the chosen article. (An extended version of this assignment was an option for the final project for the class.) Even more than I expected, students were surprised and interested to find things like the wide array of departments represented by the authors (biology, physics, computer science, various forms of engineering); the number of countries represented (with the very large US fraction being even larger among references than citations); and more. We spent a while discussing authorship — most students have a nineteenth-century notion of lone scientists writing single-author papers — and how numbers of people in research groups varies between fields. I of course showed an example from high-energy physics; this one has over three hundred authors, which is fairly typical:
The full first page:
Final project
For a final project, students had a choice of either an expanded version of the ‘follow the literature’ assignment described above, or they could write simple computer programs that illustrated biased random walks (as in bacterial chemotaxis) or logistic growth (chaotic population dynamics). They could work in groups. About 2/3 chose the programming exercises. All of these went well — better than I expected in terms of both students’ interest in the project and their success in implementing them. (The students made use of the simple programming methods they were learning in the computer science class — I cringed to watch graphs being made by having a “turtle graphics” cursor trace out paths, and had flashbacks to seventh grade.)
Overall assessments
Did the course succeed? In some ways: yes. Students seemed very interested in the topics we explored, and most weeks we had quite good discussions. And it certainly was the case that the things we learned about were, to the students, completely new and far outside the scope of standard things they had previously encountered. If this were a “normal” course, I’d call it a success based on the level of engagement and interest we achieved. However, it was not a normal course, and there were three issues with it that dampen my enthusiasm for repeating it.
First, since I taught this concurrently with my Physics of Life course, a typical large, four-credit class, it added to my workload. Of course, I knew this going in. But, because I have far, far more things to do every week than there are hours in which to do them, I should really be subtracting from, rather than adding to, my list.
Second, a goal of the FIGs in general is that they’re social as well as academic experiences, and it’s apparent that I have neither the time nor the inclination to be very social. The high point of this aspect of the course was during the first few weeks, when I made sure to have coffee or lunch with all the students, in groups of 1-5. This was fun, and it was interesting to get some insights into their very different backgrounds, levels of comfort with the university, and experiences. Especially with respect to programming, the students ranged from ones who had never programmed anything prior to their concurrent computer science course to one who had held a job as a programmer. Aside from these chats, I did one social activity outside of class, a very short hike up Skinner Butte. (I had hoped for Spencer Butte, about an hour to a rocky summit with beautiful views, but the logistics of transportation foiled us.) A few students came, along with my kids; it was a nice walk on a sunny Sunday afternoon.
Third, the demographics of the FIG weren’t really what I was aiming for. The FIG connects my Physics of Life course with the introductory computer science class; students in the FIG are enrolled in both these courses. The intended audience of the Physics of Life class is non-science-major undergraduates. Introductory computer classes, at UO and elsewhere, are attracting sharply increasing numbers of students (see here) with a very wide range of interests. Therefore I was hoping for the same diverse assortment of students in the FIG — people interested in majoring in history, or political science, or art, etc. Instead, eighteen out of twenty in the course were intended computer science majors! They were a great bunch, but they were not my target in terms of general education. One could argue that these students are precisely those who we should be introducing to quantitative biology, since the field very much needs them. I would agree with this, and if I were part of a quantitative biology program I might agree that this is part of my job. But I’m not.
Overall, I don’t intend to teach the seminar again in the near future, though I could imagine happily revisiting it again someday. In case anyone plans similar courses, hopefully the thoughts noted here are of some use — feel free to email me for more details. The topic of mathematical and physical modeling of biological systems is fascinating, and it is certainly one that more students, especially early in their undergraduate careers, should be exposed to!
The Eighteenth Elephant 