De-extincting mammoths

I had posted a picture without much content that was on of my most popular blog posts: Bring back the mammoth! and in April I noticed that Beth Shapiro (a UCSC professor in ancient DNA) had published a book, How to Clone a Mammoth.  My wife bought the book from Bookshop Santa Cruz, and I just finished reading it.

There was not much new technical material in the book for me (I’ve been to several of Beth Shapiro’s and Ed Green’s talks about ancient DNA, and I’ve read papers and heard talks on the CRISPR/CAS9 system for editing DNA), but the book is a well-written description of the technology and of the ethics involved in de-extinction. Dr. Shapiro has a fine sense of humor, so book is highly readable without the dry academic tone that mars many books written by professors.

The reading level of the book is carefully judged to be accessible to most adults (about a high-school reading level), and the content should be accessible to high-school students and advanced middle-school students.  Despite the title, the book does not contain any detailed instructions on the techniques and processes used in recovering ancient DNA or editing genomes (most of which are tedious and difficult even for the grad students and postdocs who do them routinely). It does, however, provide a broad overview of the processes involved, what their limitations are, and why one might want to recover a species from extinction besides the “coolness” factor.

Dr. Shapiro is clearly in favor of de-extincting some species, but is also very clear that what she means by this is not what some people assume. She does not believe that it is possible to bring back mammoths and passenger pigeons as they were originally. What is feasible is to recover some of their lost genes and put them into closely related species (like Asian elephants and band-tailed pigeons), to get a hybrid species that can (perhaps) fill the ecological niches vacated by the extinct species.  That is, we can’t get the original mammoths back, but we may be able to create a mammoth-like elephant that looks like a mammoth and can survive in the cold the way mammoths did.

She makes a good case for the environmental benefits for reintroducing some species to habitats that have lost them—particularly large herbivores like mammoths and giant tortoises, but she also presents the case against reintroduction fairly clearly (though her position is clear).

I highly recommend the book for high-school biology students, particularly home-schooled students, who have time to ponder some of the difficult ethical questions involved in de-extinction.

Narrowing the gender gap in CS

Today’s post collects a few drafts of pointers to articles about narrowing the gender gap in computational fields.  The first article is from CACM,  Computing’s Narrow Focus May Hinder Women’s Participation | News | Communications of the ACM:

In her position as a professor of computer science at Union College, Barr found contextualizing computer science classes led to an increase in female enrollment. “We said, ‘let’s show them that computer science can be useful by giving themes to the introductory CS courses, so students can see their relevance,’” she said. “For us, it’s been enormously successful. Ten years ago we taught the introductory course to 29 students, and 14% of them were women. This year there were over 200 students, and 39% of them were women.” Beyond college, Barr said, she’d also like to see “a bigger funnel into the corporate world and the tech industry, with people coming from many other majors. It doesn’t have to be just CS majors.”

The suggestion there is that providing interesting applications in the intro courses helps retain student interest, particularly among female students.  The  article seems to have struck a chord with some female computer scientists.  Here, for example, is a response from Katrin Becker’s blog:

A big part of what attracted me to computer science was what I could do with what I was learning. That, and that programming is largely about lists, organizing, and puzzles—all things that women often find appealing.

Personally, I think that well-designed intro courses that excite students about the possibilities of the field would serve to retain more men as well as more women, but it is certainly possible that the effect is stronger for some groups of students than for others.  Exactly what applications are chosen may make a difference also—picking applications that fit male stereotypes (car engine controllers and missile guidance systems?) may even be counter-productive in narrowing the gender gap.

Another possible explanation for why women make up such a small part of engineering and the “hard” sciences comes from an article in The Washington Post,  Catherine Rampell: Women should embrace the B’s in college to make more later – The Washington Post:

A message to the nation’s women: Stop trying to be straight-A students.

No, not because you might intimidate easily emasculated future husbands. Because, by focusing so much on grades, you might be limiting your earning and learning potential.

The college majors that tend to lead to the most profitable professions are also the stingiest about awarding A’s. Science departments grade, on a four-point scale, an average of 0.4 points lower than humanities departments, according to a 2010 analysis of national grading data by Stuart Rojstaczer and Christopher Healy. And two new research studies suggest that women might be abandoning these lucrative disciplines precisely because they’re terrified of getting B’s.

The observation is that women are more deterred from entering a field by getting low grades than men are—they found that women who got Bs and Cs in their intro courses changed majors to ones that graded more leniently, while men with low grades continued slogging along in their initially chosen major.  The data was from economics, not engineering, departments, and I don’t know whether the same behaviors apply. The article cites another study that suggests that the same behavior occurs in STEM fields:

Arcidiacono’s research, while preliminary, suggests that women might also value high grades more than men do and sort themselves into fields where grading curves are more lenient.

The suggested action is to advise women not to be intimidated by B grades.  I don’t know whether that has been attempted anywhere, but I have my doubts that just telling people not to be afraid of Bs is really going to change their strategies for maintaining their self images.  Catherine Rampell also makes a rather careless mistake in saying

Remember, on net, many more women enter college intending to major in STEM or economics than exit with a degree in those fields. If women were changing their majors because they discovered new intellectual appetites, you’d expect to see greater flows into STEM fields, too.

The mistake is in assuming that switching to and from STEM fields is equally easy.  In fact, the much larger set of required course and longer prerequisite chains make it much easier to switch out of STEM fields than into them.  Freshmen are advised to prepare for the most restrictive major they are interested in to keep their options open.  What seems to be happening is that women bail out of the tough majors at a higher level of performance than men do.

Of course, it is a mistake to think of “STEM” as monolithic entity. From The Shriver Report – 10 Reasons Why America Needs 10,000 More Girls in Computer Science:

2. Girls Are Already Making the Grade in Bio (Science)

Using AP test-taking as a measure of pipeline illustrates the true nature of STEM participation for girls. Female test-takers exceed or are close to parity with males in psychology, calculus, biology, and chemistry, but only account for 18 percent of AP computer science test takers. According to the National Center for Education Statistics, women already make up nearly 60 percent of degree recipients in biology, a whopping 85 percent in health professions, and around 50 percent in social sciences. In fact, 20 times as many girls took the AP biology test, as did AP computer science. The majority of women in ’STEM’ fields choose life sciences, so simply saying we need to increase the number of women in STEM is a mistake. Instead, we need to narrow the conversation to focus on computing and IT fields, where the shortfall is the largest.

Not only are women already over-represented in biology at the BS level, but biology has been over-producing PhDs for a couple of decades relative to the demand, so that jobs in biology research are very difficult to get and generally pay substantially less then other science and engineering fields.  There are some very high paying jobs in biomedical research, but the demand for them far exceeds the supply—the “postdoc holding tank” in biology is enormous.

I don’t have any action items coming out of these articles—I’ve already put together a freshman design course for the bioengineering majors that did hands-on, applied work providing applications for some low-level computer programming.  While I’ll continue to try to improve that course, there aren’t many lower-division courses taught by our department for majors (the others are bioethics and a no-prereq intro to biotechnology, both of which are dominated by non-majors).  The Baskin School of Engineering has just created a Computational Media department, which will take over the game design program (a predominantly male program) from CS, but which is expected to create some new computational media courses.  we’ll have to see whether these have any effect on the number of women in computational fields at our university.

Scientists need math

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.

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

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?

Bioinformatics in AP Bio, lessons released

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 http://compbio.soe.ucsc.edu/binf-in-AP/  They are released under a Creative Commons attribution/share-alike license.

Other resources people should know about include

• The AP bio home page for teachers, which has links to College Board’s resources and to the new AP biology curriculum (which affects AP bio tests starting in May 2013).  The resource list that they are collecting from and for AP Bio teachers is quite extensive.
• The BME 110 course at UCSC, which is an intro bioinformatics tools course for biologists.  It may be possible to adapt some of the assignments in that class to AP Bio.
• A page where PLoS Computational Biology is gathering resources relevant to bioinformatics in high school bio.
• My earlier post about resources for teaching bioinformatics in high school.