Mike Cantor asked:
>>1. There have been many approaches to metabolic modeling. Kinetic
>simulation and MCA seam to be the most widely used, but I have also seen
>papers on the use of , e.g. Discrete event based models, Petrinets,
>Tendency Modeling, etc. What do you see particularly promising (or not
>promising) techniques for generating what types of insights about metabolism.
I'm hardly expert on this, but my feeling is that it will be a while before
"Discrete event based models, Petrinets, Tendency Modeling, etc." can be as
usefully applied as kinetic simulation and control analysis. However, it
would be nice if someone who disagrees with this would let us know why we
should already be taking these seriously.
>>2. What are the prospects for larger-scale modeling projects like the
>NIGMS's projected 25$ glue grant for a "Virtual Cell." Is this a naive and
>premature waste of money, or will it be a fountain of knowledge.
I'm very sceptical about this, but open-minded enough to be ready to be
convinced otherwise by hard evidence. The most complete metabolic model I
know of is the model of glycolysis in Trypanosoma brucei worked out by
Barbara Bakker. Even that only includes around 60% of the numerical values
one would ideally like to include (the others having to be guessed, treated
as "negligible", etc.), but it still requires a tremendous amount of
high-quality kinetic information about the component enzymes. When I
counted several months ago I think (I'm trying to remember, not checking!)
there were about 65 separate pieces of numerical information in the model,
each of which was the result of some serious experimental effort. In the
case of whole cell models I don't believe we are anywhere close to have a
comparable amount of genuine information to feed in. I'm not ready to put
much confidence in the results from a model where few of the numbers used
are based on experimental kinetic information.
>>3. What is the relation between metabolic modeling and informatics? What
>if anything can the field of metabolic modeling gain from the genomics
>explosion?
At the moment, I feel it's the other way round. Metabolic modelling and
control analysis can contribute a lot to filling in the gaps in knowledge
left by the genomics explosion. Look, for example, at the theory of
elementary flux modes being developed by Stefan Schuster and his colleagues.
>>4. What is really the role of all of the metabolic simulation software
>packages I see out there, such as Gepasi, SCAMP, Mist, DBSolve, Kinsim,
>SCOP, etc. Are these widely used? What factors have led to their adoption?
My impression is that none of these is widely used in the sense, say, that
BLAST is widely used. However, in the limited context of people interested
in the problems that these programs can solve I think that Gepasi and SCAMP
are much the most widely used. (SCAMP, incidentally, will be superseded by
Jarnac, so if you haven't already started with SCAMP it would probably be
sensible to move straight to Jarnac. I think Jarnac can do everything that
SCAMP can do, and a lot more besides).
At a workshop in Stellenbosch last April where the students could choose
whether to use Gepasi or SCAMP/Jarnac I had the impression that the
division was close to 50/50 and that it was predictable on the basis of
what the background of the individual student was. In general people with
little experience of programming or modelling preferred Gepasi, whereas the
more experienced preferred SCAMP or Jarnac. My recommendation would be that
if you plan to do a lot of modelling it would be sensible to choose Jarnac,
but that if you just want to dip your toes in the water you'll find Gepasi
less intimidating.
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I imagine you must have been disappointed by the paucity of responses to
your questions, so I hope we can restimulate discussion of them. In my case
I planned to answer immediately but I had a meeting in the US just before
Christmas, followed, of course by Christmas itself and the New Year. So one
way and another I let it slide a bit. Probably others did as well.
athel
--
Athel Cornish-Bowden athel at ibsm.cnrs-mrs.frhttp://ir2lcb.cnrs-mrs.fr/~athel/homepage.htm
Phone: + 33 491 16 41 38; fax: + 33 491 16 45 78
CNRS-BIP, B.P. 71, 13402 Marseille Cedex 20, France
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