Students actually showed up, so we really do have to teach the course. MCB112 Biological Data Analysis is now in its first week.
The tricksiest bit in the first couple weeks is bringing people up to speed in writing Python, for people who’ve never written code before. We trust in the power of trial and error. We give working example scripts that are related to what the students are asked to do on a problem set. Developing code by mutation, descent with modification, and selection: coding for biologists.
Soon we’ll start to lift the training wheels, while trying not to leave people in a “now draw the rest of the damn owl” situation.
When you’re learning to code, with every line you type you’re looking something up. Your concentration is getting broken all over the place as you try to express the Simplest Stupid Thing (Why Don’t You Work gaaaah $%^&#@). If you’re also trying to learn something else at the same time that requires hard thinking – an algorithm, a mathematical equation, a biological analysis approach – really just about the last thing you need is to have your concentration broken every ten seconds because you can’t express yourself. The best way to learn to code isn’t to start by writing scientific code. It’s better to code something fun, something that you’re completely absorbed by, something that isn’t too conceptually difficult. You want to have only the code frustrating you, while the goal pulls you in and keeps you engaged.
But I can’t exactly recommend that students learn to code the way that I did. Sure, go get yourself absorbed in an early Internet massive military-industrial simulation game. Automate your country’s economy, re-invent Dijkstra’s shortest path algorithm to distribute your resources, make an interactive display of your map, reverse engineer the client/server communication interface so you can launch automated attacks… no, this is no way to do a PhD. Even if it does mean you end up knowing C and Perl and understanding dynamic programming, GUI development, and networked computing.
So alas, we’ll try to generate entertainment value in more socially acceptable ways, like sand mouse mysteries in the problem sets, or teasing Lior Pachter. We’ll see if it’s enough. If not, maybe I’ll have to see if the old Empire code still compiles.
I’m starting to plan a new Harvard course that’ll be called Biological Data Analysis. Biology is going through a culture change. It’s suddenly become a data-rich, computational analysis-heavy science. Are we going to outsource data analysis to bioinformaticians and data science specialists, or are biologists going to analyze their own data? There’s always advantages to specialization, and we need bioinformatics and data science. But I also feel that we are dangerously weak in training biologists to think about their own data. The usual response I get when I talk about it is something like “you can’t expect wet lab biologists to learn how to program”.
What I want to teach in Biological Data Analysis is that writing scripts and using the command line for data analysis is not software engineering, it’s just a simple and essential thing that a wet lab biologist can do, and needs to do. I’m going to teach from the point of view that biologists already have a special advantage in large-scale data analysis: we are trained to expect that we will be screwed by our experiments. We should be treating data analysis the same way. Like doing an experiment on a complicated organism, any given data analysis only gives you a narrow glimpse into a large data set. God only knows what else is going on in the data that you’re not seeing. Like doing experiments, you need to design positive and negative controls to protect yourself from the hundred different ways that nature (and computers) are going to mess with you. Writing scripts that generate positive and negative controls for a data analysis is a powerful and biologically motivated thing to know how to do.
Once you’re generating negative control data — “here’s what the data would look like if there were no effect to be found” — you’re actually doing statistics, but in an intuitive and motivated way that any biologist can understand. Instead of learning a bunch of incantations and lore about t-tests, you’re forced to think directly about what your null hypothesis is, because you have to make a negative control data set according to that null hypothesis. The “p-value” is directly the probability that you observe a signal in your negative control. This style of simulation-driven analysis is enabled by modern computational power plus the ability to write simple scripts and use the command line. You don’t have to learn statistics per se. You have to learn how to do computational control experiments. I expect that if a biologist learns the simulation-driven style of analysis first, then they’re motivated to go on to learn more serious statistical analysis as they need it… and they’re armed with a powerful way to check analytic results against intuitive simulations.
If this makes any sense to you, and if you happen to be a Harvard PhD student graduating this year, and you’re thinking it might be nice to take a year and do some teaching… boy, do I have a deal for you. Harvard has a thing called the College Fellows Program. This is a one-year position (renewable for one more) that focuses on teaching and course development. We’ve just posted an ad looking for a College Fellow to help me develop and teach Biological Data Analysis. Application deadline is April 15. Feel free to contact me directly with questions!
Without getting into identifiable details, here’s what’s happening so far in a search I’m co-chairing for the Harvard FAS Center for Systems Biology, together with Sharad Ramanathan, with some thoughts on how this process works.
Harvard’s FAS Center for Systems Biology has opened a search for a new tenure-track faculty member at the assistant professor level. Sharad Ramanathan and I are the co-chairs for the search committee.
From the ad:
The Center emphasizes quantitative approaches to fundamental problems in biology. It aims to foster interactions across disciplinary boundaries, housing faculty from a spectrum of academic departments in addition to the Bauer Fellows. Exceptional candidates in any area of quantitative biology will be considered, including those taking computational, theoretical, and/or experimental approaches.
Faculty associated with the Center for Systems Biology have access to facilities and opportunities for collaborative research not only through departments but also through the Bauer Core facilities, the Center for Nanoscale Systems, the Broad Institute, and the Center for Brain Science. The successful candidate will hold an academic appointment in a natural science department such as, but not restricted to, Molecular and Cellular Biology, Organismic and Evolutionary Biology, Physics, Applied Mathematics, or Chemistry and Chemical Biology.
The application web page is here.