Duty Calls, Science

Well, that will cause some eye-rolling: All biology is computational biology!

I met Emma Ganley from PLOS Biology at the #scidata16 conference last year, and shortly afterwards she invited me to contribute to the PLOS Biology collection Research Matters:

In this series, we ask leading scientists in their respective fields to explain clearly and engagingly for a lay audience why the research carried out in their laboratories – and those of their collaborators and their colleagues – matters.

It wasn’t immediately clear to me, what I should write about. I tend to label myself a cancer researcher nowadays, but cancer research does not need any explanation why it matters – unfortunate as that is.

At the same time, I am a computational biologist – and here I thought was a much bigger need to explain why it matters. The question is not so much why computational biology and bioinformatics are useful (nobody seems to question that it’s handy to have the geeks around) but why is it biological research, rather than just a support and service activity.

Well, I argue without computational stuff you can’t do any biology at all today.

My piece is called ‘All biology is computational biology‘ and can be found here http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050

Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.

Obviously, I wrote this through the lens of somebody working in genomics and in cancer, and my biases show in the examples I choose. I understand that there might be many field biologists out there who are perfectly happy to catch butterflies without any computer at all. But I thought if I put too many caveats and disclaimers in, the whole piece would just lose its punch.

Tell me what you think: is this just all obvious? Or am I preaching to the converted, while die-hard old-school biologists will just roll their eyes?


10 thoughts on “Well, that will cause some eye-rolling: All biology is computational biology!

  1. Hi Florian,

    I think this piece is right on point (but I’m a “converted”). Wherever you go, ecology, animal physiology etc. you cannot do biology without a computer and data analysis any more. Frankly, I think many ecologists were earlier adopters than molecular biologists (stats in R etc.), where there is still a lot to do and “pet” bioinformaticians are pulling their hairs (cooperations don’t always work without a common communication ground). Albeit the “big data” flood in molecular biology is probably the largest revolution.

    Thus, learning programming, automation, and statistics needs to be integrated into all undergraduate biology education (and probably other traditional topics removed from the curriculum to make room).

    Thanks for the publication, I’ve been preaching this for a while and will now use it as an argument (as I already do with ‘You Are Not Working for Me; I Am Working with You’).



    1. Thank you for reading my stuff.

      “and probably other traditional topics removed from the curriculum to make room” — I agree.

      But since undergrad curricula are key factors defining a field, I expect it will take some time and probably a generational change to see it happen.


  2. Hi Florian,

    “Or am I preaching to the converted, while die-hard old-school biologists will just roll their eyes?”
    There are also those biologists who tell you how important and revolutionary computation is to biology, but still wouldn’t support making room for it in their undergraduate track.
    The educational conservatism is indeed, as you mention, the main inhibitor of computational biologists becoming “just” biologists. I actually designed a course on computational thinking for life scientists (http://ca4ls.wikidot.com) which is about to start, for the fifth time, next week…



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