“Current experimental work, and biology as a whole, suffer from a serious problem–the perception that their existence is only justified as a tool for medicine.” ]]>

Still, I think I’m better off than someone who aced their first two semesters of calculus but did no modeling or proofs (as I understand, those have fallen by the wayside in many calculus courses). Put another way, I’d be less embarrassed to ask a technical wiz to compute some unwieldy integral that I’d proposed as a model for X, than if I were a technical wiz but had no “modeling imagination” (for lack of a better word). Ideally you want to be both, of course, like Fisher or Hamilton, but Darwin was pretty unsophisticated mathematically (both in how much learned math he’d mastered and in how much he used math in his work), but he had good general reasoning & observational skills.

There’s actually a worse case than the not-so-creative technical wiz: the technical wiz who doesn’t know anything, and so proposes an insane model (and not as in, “it’s crazy enough to work”). The recent “rebuttal” letter on Lahn’s work which used computational smoke & mirrors to argue for demographic causes rather than selection accounting for Lahn’s data — until you learned that (as Greg Cochran pointed out in the comments at the GNXP post) the effective population size would’ve been so small (~10) that they would’ve went extinct by now. There are evo psych arguments that you don’t need to see the sophisticated model for to know that they’re BS (e.g., treating schizophrenia as the outcome of frequency dependent selection during times of Shamanistic practices).

So I guess the boring conclusion is that it always pays to know more math, and to be more imaginative & have a better bullshit-detector. ]]>

To summarize, then, it is fine for people to be interested in mathematical models in biology. Some of these models and theories are fascinating in and of themselves, but the basic questions of biology are being answered at a wet bench, by people who can plan good careful experiments and carry them out. Most of the time, the mathematical investment in these is quite low.

That said, there are some great realms of biology where facility with data analysis tools such as statistics packages (software) are incredibly valuable. Microarray analysis, proteomics, and other “high-throughput” science is performed using some pretty sophisticated math. However, most of the time the end user can remain fairly ignorant.

Sorry, I know this is a bit of a mess. I’m a bit ambivalent about the issue, because I personally really enjoy math at many levels, and I want to use it more in my research approaches, but it just doesn’t fit in with the bulk of the “high yield” approaches. ]]>

PS: You misspelled Lubos ]]>

Thank you for the kind post. I certainly agree with your analogy of evolution and gravity. That is exactly the problem. Unfortunately, finding biology’s equivalent of electromagnetism doesn’t appear to be all that easy ðŸ˜‰

All the best. ]]>