Martin and Pedro-
While it's true that few kinetic data have yet been published for gene
expression networks, this is about to change dramatically. Large-scale
gene expression kinetics are already being recorded by a number of
high-throughput technologies (e.g. ODN chips, and automated RT-PCR). In
a policy article in Science last year, Eric Lander asserted we should be
preparing analytical techniques to handle *thousands* of state
variables. He describes "whole-genome expression studies", and he is not
talking about the distant future. Several weeks ago, I heard a lecture
and the US National Academy of Sciences by Lee Hood, who made much the
same point, although he has in mind what he believes is yet a better
technology capable, he thinks, of 3 to 5 decades of dynamic range.
So my point is, I think the data will be in hand long before we are
prepared to analyze them. My personal record for number of state
variables in a published model is less than 50, and even unpublished
I've never exceeded 100. It seems to me I once had a dream about a
model with 500 state variables.... or was is a nightmare? :)
One thing I think we will find, is that it will be far more tractable to
solve these huge problems as systems of differential equations (even for
steady states) than to wrestle with matrix algebra. I also think these
systems will be stiffer than anything we've yet encountered and the
system matrices will be amazingly sparse (thank heaven!).
Does anyone here have experience with ODE solvers that can handle
hundreds or even thousands of state variables? Has anyone explored the
computability questions surrounding such large systems?
Regards,
Bob
--
Robert D. Phair, Ph.D. rphair at ix.netcom.com
BioInformatics Services http://www.webcom.com/rphair
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