David Baltimore's advice to graduate students
Anna Kushnir
Friday, 22 August 2008 16:27 UTC
In a recent interview in Nature Reviews Molecular Cell Biology, David Baltimore, winner of the 1975 Nobel Prize in Physiology/Medicine for the discovery of reverse transcriptase, gives his thoughts on the current graduate education system in America, and suggests a few changes.
He says that graduate students in the biological sciences need to have more of a quantitative education, covering subjects such as physics and chemistry. Baltimore says, “They [grad students] need to be more facile in handling mathematics, physics and physical chemistry and in applying these skills to biological research, especially for the development of new technologies.”
He also addresses something that has been touched upon in this forum – early specialization in grad school. Baltimore recommends choosing a specialty/narrow field early on, but broadening it as the individual progresses through grad school, to post-doc and onto investigator.
What are people’s thoughts on this? Has graduate education in biology become too focused? Do graduate programs need to re-center on in chemistry, mathematics and physics in order to speed along and improve biology research?
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Replies
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Hi Anna,
My PhD project in immunology is to study biophysical properties of protein-protein interaction.I left mathematics in High School but now I realize that how important quantitative skills could have been, even if I am studying a non-quantitative subject.
Now day’s scientists are doing more qualitative study than quantitative, but next big challenging task will be to quantitatively modulate hypotheses in order to understand how the system works.
I personally believe that we should have proper mathematic training in 1st year of PhD.
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I completely agree, Amit (sorry for the slow response, btw). I could have really benefitted from some statistics training, at the very least. It doesn’t matter how biological the degree program is – one can always benefit from a strong core in math and the like. Not that it’s particularly fun, but it’s awfully useful.
There is some discussion around NN about scientists learning more diverse skills, including computing, in order to keep up with the direction science is taking. See Euan Adie’s post on the subject here. It’s more or less along the same lines as what Baltimore was talking about, I think.
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Hello,
I do agree with Dr. David’s suggestions about inputting some quantitative skills to Ph.D course curriculum. I worked with microarrays and had a quite hard time in data analysis due to lack of knowledge of stats. Once i am done with the prescribed course work, i am planning to take some courses so that i can improve my quantitative skills. One of the important part i have observed in US educational system is that once you show interest, people are willing to help in my case possibly auditing the courses rather than formally registering it.
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I do agree with anna kushir,in all aspects of biological research work the basic mathematics,biochemistry are more important.so it is necessary for any researcher to be strong on these areas for designing and technological improvement of the process.
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Yes, It’s true.
The ability for persuing knowledge is also important for us if you really want to explore a field.
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Hi Anna,
I do agree with it.
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Yes ,completely! Biology research needs many other subjects to support.
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Personally, I don’t think there should be any formal training/taught courses during a Ph.D (other than specialist methods training etc). If you have gaps to fill in your knowledge, fill them yourself. I think a Ph.D is meant to be an apprenticeship in science and it’s meant to be about learning to do things for yourself, learning to learn things for yourself and learning to bite the bullet, go out there and teach yourself to do things and to do that efficiently.
Yes, we absolutely need to use maths/stats in the biological sciences – which is why they should be taught in the first year of an undergraduate degree and should be second-nature to all biologists – not some alien concept we’re scared of using.
Think of it this way – you’re studying say, botany, and you want to try using a new (to you) method like RNA extraction which you’ve not done before. Do you need to sit through months of a taught course on RNA extraction? Of course not. You read papers, find out which methods other people use, find a method which works on the type of plant you work with and then give it a go. See if it works. If it doesn’t, you might want to try contacting someone who does do it on a daily basis and see if they know what’s going wrong. Statistics is the same. You read papers, find out what methods other people use, find a suitable method, try it, see if it works, if not, you contact someone who does it a lot and see if they can spot where you’re going wrong.
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