>To: btk-mca at net.bio.net>From: Bob Phair <rphair at ix.netcom.com>
>Subject: Re: MCA - Diffusion of Innovation
>Date: Thu, 01 May 1997 09:33:52 -0400
>>To me the vital question is: Why has modeling failed to gain the
>position in biology that it has in, say, physics or engineering?
>>Sol Snyder, a distinguished neuroscientist and an acquaintance of mine,
>could never understand why I wanted to build models. He said, "All the
>important discoveries of biology have been qualitative."
>>What we have failed to make plain to the mainstream, is the power of
>modeling to make qualitatively important discoveries. Only when we make
>a seemingly outlandish, but genuinely important, assertion that is
>subsequently confirmed by experimental test can we hope to make a deep
>impression.
To turn this on it's ear, the answer is also that biology has not sought to
*ask* quantitative questions in many cases, and the complexity of biological
processes and difficulty in obtaining good quantitative measurements have
made applications of mathematics difficult at best. This occurs because the
trainning required of a 'true' biologist is such that very few of them also
manage to learn much math--this is not a disparagement just a result of the
complexity of biology. A mathematician, on the other hand, by focusing on
and learning narrow areas of biology, can bring insight. *But* I think that
many experimental biologists think that the processes are so complex as to
be impossible to model, and "Who is this upity modeler who thinks he/she can
learn enough about biology to describe it with math in a few months, when I
have been studying this all my life and I know that even the greatest experts
don't understand the biology completely." (This is essentially what I was
told when I proposed the development of a model of the menstrual cycle.)
The best response is: we learn the most from models when they fail, because
that disproves hypotheses.
I've been presenting the 1st half of my menstrual model, and some eminent
endocrinologists have been getting very excited about it. (Paper about to
be submitted.) So don't be all doom-and-gloom, Bob.
-Paul
