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2013 April 29

Scientists need math

Filed under: Uncategorized — gasstationwithoutpumps @ 14:28
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At the beginning of April (but not on April Fool’s Day), the Wall Street Journal published an essay by E.O. Wilson (a famous biologist): Great Scientists Don’t Need Math. The gist of the article is that Dr. Wilson never learned much math and did well in biology, so others can do so also:

Wilson’s Principle No. 1: It is far easier for scientists to acquire needed collaboration from mathematicians and statisticians than it is for mathematicians and statisticians to find scientists able to make use of their equations.

Wilson’s Principle No. 2: For every scientist, there exists a discipline for which his or her level of mathematical competence is enough to achieve excellence.

The first principle is probably true, but is more a sociological statement than one inherent to the disciplines: applied mathematicians and statisticians welcome collaborations with all sorts of scientists and are happy to learn about and work on real problems that come up elsewhere, while biologists (particularly old-school ones like Dr. Wilson) tend not to be interested in anything outside their own labs and those of their close collaborators and competitors.

The second principle is possibly also true, though much less so than in the past.  Biology used to be a major refuge for innumerate scientists, but modern biology requires a really strong foundation in statistics, far more than most biology students are trained in. The number of positions for innumerate scientists is rapidly shrinking, while the supply of innumerate biology PhDs is growing rapidly.  In the highly competitive job market for biology research, those who follow E. O. Wilson’s advice have a markedly smaller chance of getting the jobs they desire. Of course, Dr. Wilson seems to be unaware of the decades-long oversupply of biology researchers:

During my decades of teaching biology at Harvard, I watched sadly as bright undergraduates turned away from the possibility of a scientific career, fearing that, without strong math skills, they would fail. This mistaken assumption has deprived science of an immeasurable amount of sorely needed talent. It has created a hemorrhage of brain power we need to stanch.

An undergrad degree in biology (even from Harvard) has not gotten many students much more than low-level technician jobs for most of that time (admission to grad school is the better option, as biology PhDs have been able to get temporary postdoc positions at least).  Perhaps Dr. Wilson considers a dead-end job at little more than minimum wage a suitable scientific career—many others do not.

Dr. Wilson does make one unsubstantiated claim that I agree with:

The annals of theoretical biology are clogged with mathematical models that either can be safely ignored or, when tested, fail. Possibly no more than 10% have any lasting value. Only those linked solidly to knowledge of real living systems have much chance of being used.

Biology is a data-driven science, not a model-driven science (a distinction that physicists trying to jump into the field often miss).  Most of “mathematical biology” has been an attempt to apply physics-like models in places where they don’t really fit.  But there has been a big change in the past 10–15 years, as high-throughput experiments have become common in biology.  Now mathematics (mainly statistics) is needed to make any sense out of the experimental results, and biologists with inadequate training in statistics end up making ludicrously wrong conclusions from their experiments, often claiming high significance for random noise.  To understand the data requires more than Wilson’s “intuition”—it requires a solid understanding of the statistics of big data and multiple hypotheses, as humans are very good at perceiving patterns in random noise.

I was pointed to Dr. Wilson’s WSJ essay by Iddo Friedberg’s post Terrible advice from a great scientist, which has a somewhat different critique of the essay. He accuses Wilson of “not recognizing the generalization from an outlier cannot serve as a viable model, or even an argument to support his position.”  Iddo makes several other points, some of them the same as mine—go read his post! Of course, like me, Dr. Friedberg is a bioinformatician and so sees the central role of statistics in 21st century biology.  Perhaps the two of us are wrong, and innumerate biologists will again have glorious scientific careers, but I think the odds are against it.

2012 July 10

Genetics “Why Do We Have to Learn This Stuff?”

Filed under: Uncategorized — gasstationwithoutpumps @ 21:01
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Rosemary Redfield (Department of Zoology, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada) has recently published an article in Public Library of Science Biology:  “Why Do We Have to Learn This Stuff?”—A New Genetics for 21st Century Students.

The article outlines problems she sees with the disconnect between the traditional undergrad genetics courses and what biology students really need to learn about genetics.  She also outlines a new course that she thinks should replace the traditional genetics class.  (She does not say whether she has convinced her colleagues at UBC of the wisdom of such a change.)

After outlining the syllabus for the new course, she admits that she expects some pushback:

This radical a change will encounter lots of obstacles. For many geneticists the most upsetting change will be the demotion of genetic analysis from its reigning place in the curriculum. Genetic analysis used to be the most powerful tool for understanding how organisms work, and thus the best skill we could give our students, but its research role has been largely supplanted by molecular methods.

Here, stripped of all the explanation and justification that Prof. Redfield provides, is the core of her proposed curriculum:

Box 4. Suggested Syllabus for a 21st Century Genetics Course

  • Personal genomics
  • Natural genetic variation in populations (humans and others)
  • Structure and function of genes and chromosomes
  • Genetic variation arises by mutation
  • Genetic variation and evolution (selection for function, phylogeny, homologs, gene families)
  • How genes affect phenotypes: pathways, regulatory interactions, heterozygosity, dominance effects (several classes)
  • Genetic variation also arises by chromosome reassortment and homologous recombination
  • Mitosis and meiosis: mechanisms and genetic consequences (several classes)
  • Mating: mechanisms and genetic consequences
  • Linkage and sex linkage
  • Genetic analysis: investigating gene action using inheritance of simple (“Mendelian”) alleles and phenotypes in crosses and pedigrees (several classes)
  • Organelle genetics
  • Epigenetic inheritance
  • Genome structure, function and evolution; causes and consequences of chromosomal changes (several classes)
  • Phenotypic effects of natural genetic differences, heritability
  • Genome-wide association studies and related studies linking genes to phenotypes (several classes)
  • Genetics of cancer; inheritance of alleles affecting risk

I rather like the idea of starting genetics from a population view, rather than a Mendelian analysis of phenotypes, but I think that personal genomics should come a bit later.

Of course, I’ve never taken a genetics class, so I don’t really know which parts of it are essential (or even what is in the standard course). One concept that does seem to be important in reading papers about genomics is the notion of chromosomal distance in centimorgans.  That seems to still be there in the middle of the class (in the lectures on linkage).

I’m curious what people who teach genetics courses or require them as prereqs for their classes think of this shift in emphasis of genetics courses.  What would it gain? What would it lose? Has this sort of change to the course already been tried?

2012 June 27

Bioinformatics in AP Bio, lessons released

Filed under: Uncategorized — gasstationwithoutpumps @ 11:03
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As those who have been reading my blog for a while know, I’ve been working with UCSC grad students to develop materials for bioinformatics lessons for high school biology classes.  I have a series of posts about Advanced Placement Biology courses and the AP Bio exam.

In a previous post about the project, I described our goals:

  • The primary goal is to teach students biology, not computer science or bioinformatics.  The bioinformatics should be good support for the underlying biology lesson.
  • Whatever we produce should be made available on the web (but putting any answer keys behind password protection, should we end up producing anything that needs a key).
  • The students will present the lessons to the class (both to expose the high school students to college student role models and to give the grad students practice teaching), but the lessons should be teachable by non-bioinformaticians.  In particular, the high school teacher should be able to teach it himself next year.
  • If things work out well, it might be worth presenting a paper explaining the project (and advertising the materials) at a high school biology teachers conference (perhaps an NABT conference?).

We have just released the two lessons we’ve developed so far: one on genetic diseases, the other on phyogenetic trees.

Each was tried at one school, in 3 sections of AP Bio (where AP bio is a required course for all students).  The lessons took one block each (just under 2 hours for a block), with some sections finishing everything with time to spare and others not quite finishing.  (Consistently the first section getting the lesson having trouble finishing and the third one having time to spare—I don’t know if the difference was in the speed of the initial presentation and our quickness in responding to problems or in the competence of the students.  There was more assistance available to the students for the first two sections, which were also the slower two.

The resources can be accessed directly from  They are released under a Creative Commons attribution/share-alike license.

Other resources people should know about include

2012 April 8

Princeton University’s Integrated Science course

Filed under: Uncategorized — gasstationwithoutpumps @ 13:33
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I recently heard about the Integrated Science course at Princeton University, though they’ve apparently been running it since 2004.  The class is rather heavily crosslisted: ISC/CHM/COS/MOL/PHY 231, 232, 233, 234.  The class must be somewhat expensive to run, as it involves a dozen top-notch faculty from chemistry, physics, biology (both molecular and evo-eco), and computer science.

I’m usually a bit dubious of integrated science classes, as they tend to be an excuse for sacrificing depth for breadth.  This course does not seem to have that problem. In fact, it looks more like an honors course, requiring students to come in with strong high school backgrounds in chemistry, physics, and calculus (BC, not just AB).

They use computer modeling (both dynamical models and statistical models) extensively, which makes me wonder why they are using Halliday as the physics text, rather than Matter and Interactions—was it just familiarity with the book, or are there some good pedagogical reasons for preferring Halliday in the course?  It may be because they actually get a little into quantum mechanics at the end of the year, which Matter and Interactions does not cover well.

Somewhat unusually, the course has a heavy component of computational biology (mainly systems biology, it seems, but a little on finding genes and comparing them between yeast and human).  Overall, it looks like a pretty intense course for a first-year college class (it counts in total as 4 courses at Princeton, and is supposed to be the equivalent of first-year chem, first-year physics and the first semester computer science).

There is a second year integrated science course that covers organic chemistry and biochem.  The six courses look to me like excellent preparation for a student going into bioengineering or bioinformatics, though students wanting to do traditional physics or chemistry might be better off with a narrower focus their freshman year.

I don’t think UCSC can currently afford to teach honors courses like this one, as much as I would like us to.  It is a shame, but it seems like honors courses are the first thing that gets cut when department budgets get squeezed. I can understand why, because relatively few students are affected and honors classes, by their very nature have low student/faculty ratios, so are expensive to run.  But the loss of honors classes causes a disproportionate loss of top students to other universities—a problem UCSC administration has worried about for quite some time (our attrition is bimodal, with an unusually high loss of top students as well as the more common loss of students failing out).  Unfortunately, the loss of top students never seems to worry the administrators enough to actually create and maintain honors courses.  Honors courses get taught occasionally, but they never last long—the administration only ever provides startup funds for such classes, never continuing budgetary allocations.

The Princeton course looks like a good model to follow, if we ever had the money to do it.  Princeton, of course, has a huge endowment and high tuition, so they can afford to continue expensive courses if they work (they’re in their 8th year of teaching this course).  I doubt that UCSC could scrape together the funds to offer such a course even once, though the probably could put together a watered-down large-lecture version of it, if there were any point to doing so.

2012 February 25

PLoS Computational Biology: Bioinformatics: Starting Early

The Public Library of Science has a a service where they group together open-access articles from their journals to make  PLoS Collections: Article collections published by the Public Library of Science.

One collection that might be particularly interesting to readers of this blog is PLoS Computational Biology: Bioinformatics: Starting Early, which has articles from PLoS Computational Biology about getting bioinformatics into high-school courses.  So far, there are only 4 articles, all from the 27 October 2011 issue of the journal, but I expect that more will come out in the next year.

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