Antedisciplinary science

Bioinformatics and computational biology are very appealing to a certain type of scientist. Unanswered questions in the life sciences are some of the most interesting and important in the world, and bioinformatics offers a novel approach to answering these questions. This novel approach requires a firm grounding in multiple disciplines, ranging from molecular biology to computer science to statistics to genetics to engineering and everything in between.

Because bioinformatics is such an interdisciplinary field, I often find myself wishing I had more time to learn more things so that I could be more of an “expert” in more areas related to bioinformatics. I do a lot of programming, but I wish I had the time to truly develop the skills of a professional software engineer. I use statistics frequently in my research, but I often lack intuition when it comes to solving even basic problems of probability. Most of my research is related to genetic and genomic sequence data, but my bench skills are so rusty that I wouldn’t trust myself to handle expensive sequencing reagents until I’ve had a few months’ refresher in the lab. The problem is that I don’t have enough time to do a PhD in 3 (or 7!) different traditional scientific disciplines.

I came across this article in PLoS the other day that I thought really addressed this issue well. The author, like me, has a skill set distributed across several “traditional” scientific disciplines. He is an “expert” in his area of research, but his area of research does not fall nicely into any one category. He suggests that just because someone is not an “expert” in any one traditional discipline does not mean they cannot make useful individual contributions to science. In fact, they can make unique individual contributions that otherwise would require the collaboration of “experts.” It’s definitely a good read and gave me some confidence as I press forward in my interdisciplinary (or should I say antedisciplinary) graduate studies.

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