I think the queston of why MCA has not really picked up in the US
does centre around the Journals involved but not in a
"theory/experimetal" journal way. The journals that MCA articles are
most frequently published in are European-based i.e. Eur J Biochem,
Biochem J, etc.
To my knowledge, there are very few MCA articles that have been
published in the front-line US biochemical journals i.e. J Biol Chem,
Proc Natl Acad Sci, Biochemistry. The only articles I can remember on
MCA in these Journals have been on MCA with reference to
mitochondrial diseases - one in J Biol Chem, and one from Attardi in
PNAS, this year. So the US/Euro bias may come from the simple fact
that not many US biochemsits are that frequently exposed to MCA in
their "favourite" journals.
> Date: 1 May 1997 07:06:28 -0700
> From: schlosser at ciit.org (Paul M. Schlosser)
> Reply-to: schlosser at ciit.org (Paul M. Schlosser)
> To: "bionet.metabolic-reg mail newsgroup" <bionet-news at dl.ac.uk>
> Subject: Re: MCA - Diffusion of Innovation
> >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
Simon Eaton
Research Fellow, Unit of Paediatric Surgery
Institute of Child Health
30 Guilford Street, London WC1N 1EH
Phone +44 171 242 9789 ext. 2254/2247
Fax +44 171 404 6181
