You might enjoy this related quote from Jacques Monod - Nobel laureate
and considered by many as the "father of molecular biology":
"We call these [mutation] events accidental; we say that they are random
occurrences. And since they constitute the /only/ possible source of
modifications in the genetic text, itself the /sole /repository of the
organism’s hereditary structure, it necessarily follows that chance
/alone/ is at the source of every innovation, of all creation in the
biosphere." [Monod 1972]
I submit that the software engineering is far from a "chance alone"
endeavor.
Grant
Russ Abbott wrote:
See abstract below. The article is open source
(http://www.pnas.org/content/107/20/9186.full.pdf+html) if anyone is
interested.
The conclusions are
"The^ authors interpret these differences in terms of two design
principles.^ The need for cost-effectiveness (or reusability) is
central^ in programming, and robustness—that is, resistance to^
breakdown due to failure of a part—is the driving factor^ in
biological systems. Evolution, they speculate, goes from^ top to
bottom in software, but from bottom to top in biological^ systems."
I'm not sure I believe that either the comparisons or the conclusions
are completely valid. But it's an interesting comparison. Software
evolves at all levels. But doesn't biology also? Aren't lower level
functions perfected after being incorporated into higher level
entities? It seems to me that biology is just messier and less well
designed. No one refactors biological systems. But it seems like the
redundancy produces more robustness.
-- Russ
---------- Forwarded message ----------
From: *Science Editors' Choice* <ale...@info-aaas.org
<mailto:ale...@info-aaas.org>>
Date: Thu, Jun 3, 2010 at 12:16 PM
Subject: Science CiteTrack: Editors' Choice: Highlights of the recent
literature
To: rabb...@calstatela.edu <mailto:rabb...@calstatela.edu>Systems Biology:
Figure 1
/E. coli/ (left) and^ Linux (right) networks
CREDIT: KOON-KIU YAN AND NITIN BHARDWAJ
^
Yan /et al./ have compared the transcriptional control network^ in the
bacterium /Escherichia coli/ to the network depiction (known^ as the
call graph) of the Linux kernel, which is the central^ component of a
highly popular operating system. Both systems^ feature (i) master
regulators (yellow in the graphic), which^ send directions to targets;
(ii) middle managers (red), which^ both send and receive orders; and
(iii) workhorses (green),^ which are controlled but do not control
others. For the bacterium,^ there are lots of workhorses but
relatively few regulators at^ the other levels. The Linux call graph
is top-heavy or more^ populated at the master regulator and
middle-manager levels.^ In other words, a workhorse in the
transcriptional network usually^ has only a few supervisors, but in
Linux, a workhorse answers^ to a large number of regulators. The
authors also contrasted^ evolution in the two systems by looking at
the functions that^ persist in 24 versions of the Linux source code
relative to^ genes that persist in 200 phylogenetically distinct
bacteria.^ For /E. coli/, the workhorses showed the greatest
persistence,^ whereas for Linux, there was persistence at all three
levels,^ but mostly in the master regulators and middle managers. The^
authors interpret these differences in terms of two design
principles.^ The need for cost-effectiveness (or reusability) is
central^ in programming, and robustness—that is, resistance to^
breakdown due to failure of a part—is the driving factor^ in
biological systems. Evolution, they speculate, goes from^ top to
bottom in software, but from bottom to top in biological^ systems.^
/Proc. Natl. Acad. Sci. U.S.A./ *107*, 9186 (2010). [One pixel image]
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lectures, archives, unsubscribe, maps at http://www.friam.org
