From: Robert J. Bradbury (bradbury@www.aeiveos.com)
Date: Fri Oct 22 1999 - 21:49:29 MDT
On Fri, 22 Oct 1999, Skye Howard wrote:
> what about engineering some of those artificial chromosomes
> to create artificial structures- for example, an implant
> of some kind that would be inherited.
Unless you put the AC into the germ line cells it will
not be inherited. However doing this is what Greg Stock
and John Campbell are proposing. (By puting an AC
into the first cell of an embryo, it will be inherited by the
germline cells as the fetus develops.
As far as ACs creating hard nanotech structures it is
doable. Think, teeth & bones & sea shells. However
since these are patterned on the scale of eukaryotic
cells (10 micron scale) and the smallest we could probably go
is bacteria (1 micron scale), it is doubtful you could
get a cell to make a nanoassembler without a *lot* of
work designing new genes that would self-assemble into
an assembler.
> I mean, for example, you could enter the
> specifications into a computer as the "desired output"
> and then running it through one of those genetic
> algorhythm programs. What better application for
> genetic algorhythm programming than genetic
> engineering?
This is similar to what I have proposed in my NanoParts@Home
scheme. If you have a description for something and a
SETI@Home type distributed program that can generate
random collections of atoms and do genetic evolution
of the designs until they meet the criteria specified by
the description.
So, I could use this to "evolve" an "atomic" rivet
or an enzyme with a known structure-function. I can't
use it to evolve a nanoassembler because we don't have
a complete description for a nanoassembler. (So the
genetic evolution algorithm has no way of selecting
the design "successes").
> A) Genetic engineering of mechanical devices (possible
> jumps on nannotech using cell replication methods?)
> (inheritable implants?)
Almost everything going on inside cells involves
"mechanical" devices at the molecular scale. The
reason enzymes work is that they position molecules
in close mechanical proximity and apply directed energy
(chemical or mechanical) to drive reactions forward much
faster than they would normally occur.
It is best to keep separate 5 things:
(1) molecular nanotechnology (atomic or small molecule manipulation)
(2) self-assembly (molecules that put themselves together)
(3) self-replication
(4) easily programmable nanoscale machines
(5) wet/soft vs. dry/hard nanomaterials
(6) self-modification
Cells do 1-3 with wet nanotech. What nanotechnology is all
about is doing 1-4 with dry/hard nanomaterials meaning you have
to solve 1 & 2 with an entirely new toolset and assembly
process. We are trying to compress what nature took a
few billion years or so develop into a couple of decades.
It worth noting that *most* of the economic benefits
attributed to nanotechnology can be obtained by mastering
(3) and perhaps (2). The degree to which (4) is required
for this is open to discussion. Having (1) with hard nanotech
materials is only the frosting on the cake. Interestingly
enough, I think it is (4) combined with (6) on top of
(1-3) in hard nanotech that raise all of the really nasty
outcomes frequently discussed on this list. What you
are suggesting in genetic evolution of hard nanotech
comes dangerously close to that. It depends entirely
on whether you have "safeties" in the selection
criteria that only select variants that are not
dangerous to their operating environment.
> b)applications of genetic algorhythm programming to
> genetic engineering.
This is already done without the "programming". There are routine
experiments that are done in biotech labs where they "evolve" better
enzymes or tools for specific purposes. It simply involves methods
to create millions or billions of variants and then selecting
the best of the bunch, mutating those and repeating.
Generally it is called "directed evolution" or something
similar and you can probably find dozens of articles on
it in Medline.
Since in biology working with "billions" is easy while in
computers it is still pretty difficult, biology will
be a better approach than computers for a few more years.
Robert
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