My appologies if you have received this posting twice. It seems that the
previous posting, sent via email to btk-mca at dl.ac.uk got lost in limbo. This
was posted directly to bionet.metabolic-reg.
eanv20 at castle.ed.ac.uk (John Woods) wrote:
>Substance:
>Kacser and Acerenza have recently published a `Universal method for
>achieving increases in metabolite production' [Eur. J. Biochem.
>216:361-367 (1993)]. So that's that, then. Better find a different
>field, folks. Of course, you might like to air any misgivings about
>the title, or indeed the rest of the paper.
{stuff about [Form: deleted}
>Substance:
>Personally, I feel the paper presents a fairly general but possibly
>not truly universal method. Although the explanation is relatively
>simple, and the conceptual operations involved reasonably
>straightforward, I am not yet conviced that a straightforward
>implementation would be feasable either computationally or
>experimentally.
computationally: it only requires to get the null-space of the stoichiometry
matrix of the system involved
experimentally: requires identification of the pathways structure,
(using the above, for example) and measurement of some fluxes.
It seems much more straightforward than MCA!
>And as it looks as if this is just a systematic application of the
>branchpoint theorem,
I don't know where you recosigned the branch point theorem in this paper. If
'branch point theorem' means 'the sum of the input fluxes is equal to the
sum of the output fluxes', this is just a consequence of the definition of a
steady state, it has nothing to do with control. Remember that this is about
LARGE changes, therefore completely outside the scope of MCA.
>In fact it seems to me
>that as more and more computerized optimization problems are being
>solved with the so-called `genetic algorithms', we might as well
>concede the point and accept that repeated rounds of mutation and
>selection are as good as we are going to get as regards a truly
>universal method --- even if we make an effective shortcut by
>simulating a few million years of evolution by computer before
>molecular intervention to bring the organisms `up to date'.
Genetic algorithms (GA) are one form of doing it, given a complete knowledge
of the kinetics of each enzyme (and possibly of the whole genetic expression
apparatus). This is IMHO not available for any bug, let alone higher organisms.
I also have in mind Stuart Kaufman et al views on evolution as a very
rugged landscape, and since mother Nature is not perfect it does get stuck in
local minima. The idea around Kacser & Acerenza (or indeed other approaches to
optimize fluxes) is to give the system (bug) a big kick in the right direction
(although only quantitative, in this case).
I my opinion this paper is going to reach as high a citation rate as Kacser, H.,
Burns, J.A. (1973) The control of flux. Symp. Soc. Exp. Biol. 27, 65-104.
Pedro Mendes
prm at aber.ac.uk
