Last Friday, the Wall Street Journal (WSJ) published an essay by E.O. Wilson that has since generated much discussion from readers (229 comments to date) on the WSJ website and also among mathematicians. The most provocative part of the article is the headline, “Great Scientist ≠ Good at Math.” (As observed by Barry Cipra, the inequality ≠ is arguably correct, but it should really be written more strongly as “Great Scientist > Good at Math,” as in “to be a great scientist, you need to be more than good at math.”) The essay itself is less provocative than the headline, and one of the points that Wilson is trying to make is that students who are not especially good or outstanding at mathematics but yet passionate about science may turn away from serious work in the sciences. He himself had little training in formal mathematics but worked with many mathematicians.
Here are a couple of interesting quotes:
“Many of the most successful scientists in the world today are mathematically no more than semiliterate.”
“Over the years, I have co-written many papers with mathematicians and statisticians, so I can offer the following principle with confidence. Call it 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.”
“Newton invented calculus in order to give substance to his imagination. Darwin had little or no mathematical ability, but with the masses of information he had accumulated, he was able to conceive a process to which mathematics was later applied.”
I was struck by these comments in light of our MPE2013 efforts. After all, one of our primary purposes is to showcase the necessity of using sophisticated mathematics to solve hard problems. We (although I am most likely preaching to the choir) know for example, mathematical techniques are vital in understanding things like DNA genomic analysis, image processing and other problems in biology. Surely, Wilson must be aware of these developments.
Recently, at the AIM workshop “Mathematical problems arising from biochemical reaction networks,” mathematicians as well as researchers who are closer to the experimental side of systems biology came together to tackle the analysis of biochemical reaction networks arising in systems biology. This workshop was really a counterexample to the above Wilson Principle No. 1.
One of the workshop organizers, Jeremy Gunawardena, gave a wonderful talk about present and past work, with the mantra that “Biology is more theoretical than physics.” The idea was that mathematical analysis of biochemical networks may be feasible, using methods from computational algebra, algebraic geometry and dynamical systems, and that mathematical methods may be the only way to understand these highly complicated systems.
One of the other common points made in comments is that perhaps Wilson is using a narrow view of what mathematics is. His view seems to be that mathematics is calculus and differential equations. Many of the elements that he thinks are important for scientific research – being a good observer, creativity, concept formulation – we think are also important elements of mathematics research.
I am sure one can point to many examples where Wilson’s principle fails to hold, but if there is a lesson from the Wilson article, it is that there is much work to do to make the message of MPE2013 heard, not just to the general public but to scientists as well.