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2012 February 9

Thoughts for revising a grad curriculum, part 2

Our department is thinking of doing a major overhaul of our graduate curriculum. This is the second in a series of posts on my thoughts about curricular design (the first was Thoughts for revising a grad curriculum, part 1).  I am hoping to get pushback from colleagues around the world, former grad students, and even current grad students, so that our new curriculum design is the best we can do within existing constraints.

The first post was pretty generic, and could be applied to almost any field.  In this post I want to concentrate a bit more on things specific to the fields our department covers.  We started out just doing bioinformatics, with the intent of eventually branching out into biomolecular engineering, and having two different grad programs in the department.  Because of various constraints put on our department, our department grew much slower than originally planned. (One of the biggest constraints was imposed by a former dean: we had to hire a big-name chair before we were allowed to do any junior recruitments.  He didn’t provide resources big enough to actually land a big-name chair, though we had serial 2-year negotiations with each of a few promising candidates, before each decided that the dean was not serious about providing any support for growing the department, so we had an effective hiring freeze for several years.)

We added biomolecular engineering eventually anyway, but with fewer than half the faculty that we wanted in either bioinformatics or biomolecular engineering.  To avoid having to go through the 5-year-process of creating a new grad program, we shifted our original grad program from “Bioinformatics” to “Biomolecular Engineering and Bioinformatics”.  So one design constraint on our program is that the graduate program has to accommodate both biomolecular engineering grad students and bioinformatics grad students in the same program.  We can (and currently do) have different tracks through the program for different concentrations, but they are not independent programs as they were originally envisioned.

I like to distinguish (mildly) between “bioinformatics” and “computational biology”.  The tools used are the same, but the emphasis is slightly different.  In computational biology, the emphasis is on the biology, with the bioinformatic tools being just a means to analyze data for a biological question.  In bioinformatics, the tool is the central object of study, with the biological questions it can answer being proof of its value.  (One gets the same sort of distinction in other fields, like microscopy, where the emphasis can be on improving the state of the art or on using microscopes and microscopy techniques developed by others to look at biologically interesting things.)

When we started, as a very tiny department whose initial faculty were all engineers, we concentrated on bioinformatics.  As we have grown (from a tiny department to a small one), we’ve added some biologists, which has changed the culture of the department somewhat.  (Engineering grad programs and biology grad programs have very different view on the right ways to choose and train grad students.)

Each of our biomolecular engineering faculty has rather different needs for the training of their students, since they are in such disparate fields (stem cells, vaccine design, sequencing technologies, biosensors). Our initial attempts at creating a core curriculum for the biomolecular engineers has not been as successful as our core curriculum for bioinformatics, and we are seeing a need to redo the bioinformatics core, so the biomolecular core must be in even more need of fixing.

One decision we made when we were just starting out was to concentrate on “tool-building” rather than “tool-using” as the core of our program—to do the original research of engineering new tools, rather than the mere application of them to scientific questions (see my post Engineering vs science).  But students from the science departments (particularly biology departments, but also ocean sciences and chemistry) are now interested in learning how to use bioinformatics tools, and there is now enough demand for that education that we should provide more of it than we currently do.  We could get larger enrollment in our grad courses if we found ways to attract and successfully teach students from other departments—students who may not have either the background or the mindset that we expect of the students in our program. As the number of available tools in our field has grown, it has also become more important for us to teach even tool-builders about the existing tools, so that they don’t end up re-inventing the wheel (a serious problem in many bioinformatics programs based in computer-science departments, based on the papers I’m sometimes called on to referee).

So some questions we need to address in our redesign of the grad curriculum include

  • Do we wish to retain a focus on bioinformatics (where we have been very strong)? or branch out into computational biology (where we’ve had some good papers, but generally as spinoffs of bioinformatics development)?
  • Do we add a computational biology track for our MS students? (That is, a mainly-courses approach for learning how to use existing tools to answer a variety of biologically interesting questions.)
  • Do we add a computational biology track for our PhD students?  I think that some of our students are already doing theses that mainly use other people’s tools, rather than developing their own tools, though we are not providing initial training towards this as a goal.
  • If we add computational biology courses, will they be primarily service courses for other departments or training for our students?
  • How do we integrate the biomolecular engineering students grad students fully into the department?  It would be a real shame to have them disappear into faculty advisers’ labs for 4 years, and not interact with the other grad students. (This is already a serious problem with our 10 faculty currently spread over 4 buildings, and soon to be spread over 5 buildings.)
  • How many different tracks through the program do we want to describe and maintain?
  • Do we even want tracks? Is our advising strong enough (and our students disciplined enough) to allow students to craft their own programs out of courses we offer, or do we need to provide very clear requirements?  (For that matter, would some advisers take advantage of students having too much freedom, to advise them take only a very narrow set of courses of use to the one adviser’s research, rather than getting a broader education that would prepare them better for their future careers?)
  • Are there any core subjects that all our students should take, regardless of their eventual research? Having a common core helps develop camaraderie between the grad students and leads to fruitful collaborations between labs (grad students are often the vectors for the infection of collaboration).
  • Are there further subjects that are core to different tracks?
  • What do we do about subjects that we see as essential, but that do not fit the campus’s one-size-fits-all grad-course size (35 lecture hours, with about 100 hours outside class)?  Should we create mini-courses?  Portmanteau classes that have multiple, nearly independent topics?
  • How will the revised graduate curriculum support the (fairly small) number of bioinformatics undergrads, who currently are expected to take 2 or 3 of the first-year grad courses?
  • Is there a way to get any synergy between the graduate biomolecular engineering courses and the (fairly large) number of bioengineering undergrads?

As you can see, I still have more questions than answers at this stage of the curricular redesign.


  1. I find the compbio MS very appealing. This could start as a designated emphasis/grad minor, able to be attached to other degrees, much to their advantage. If I dare say it, this might be something that could be coordinated with the extension program on the same topic (but, going significantly beyond, of course), resulting in an MS perhaps available over the hill. This would be an excellent addition to the valley, and could be a big outreach plus for the department.

    Comment by Richard — 2012 February 25 @ 11:39 | Reply

    • Is there a need for a bioinformatics (tool-building) MS as well as a compbio (tool-using) MS? Should we separate them into different tracks or have a single set of requirements that can lead to either a tool-building or a tool-using MS, based on choices made within the single framework?

      Comment by gasstationwithoutpumps — 2012 February 25 @ 12:53 | Reply

  2. […] about curricular design (earlier ones were Thoughts for revising a grad curriculum, part 1 and Thoughts for revising a grad curriculum, part 2).  The first post was applicable to almost any field, and the second post got more specific about […]

    Pingback by Thoughts for revising a grad curriculum, part 3 « Gas station without pumps — 2012 February 26 @ 15:19 | Reply

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