Computing vs. Genetics - Advancing Artificial Intelligence Through Biological Process Applications - page 178

significant statistical data. On other occasions,
researchers may come across a series of the style:
complain, complainant, complaint, plaint, plain-
tive, plaintiff, and may be fortunate enough to
know the meaning of its common factor, in this
case, “plaint”, because it is associated with some
hereditary particular or other that has already
been studied at length. They will then be able
to venture to try out new rules of construction,
new combinations, taking the same common root.
This is how the cartouches helped to decipher
hieroglyphics. In other words, genes, like the
words of the language can be grouped by fami-
lies, the words of different human languages that
revolve around the same concept share the same
root. Likewise, it is to be supposed that the genes
whose sequence is similar fulfil similar functions.
As a matter of fact, it now appears that genes that
resemble each other come from one and the same
ancestral gene and form families of genes with
similar functions. These are termed multigenic
families, whose genes may be disseminated across
several chromosomes. Often, neighbouring genes
have a similar spelling even if they do not start
with the same letters. And homonymic genes are
often synonyms, genes that are written differently
but have similar meanings. This will mean that
they can be used like puntales (a fragment of plain
text associated with a fragment of ciphered text)
were used to decipher secret codes.
The analysis will be gradually refined and fine-
tuned. Finally, we will know how to distinguish the
equivalents of linguistic synonyms. By detecting
the common roots, we will even learn to trace
back the genealogy of certain genes, i.e. follow
their evolutionary lineage. Complex biological
functions like breathing, digestion or reproduc-
tion are assimilated to sentences whose words
are inscribed on different pages of the genomic
dictionary. Now, if there is a irst topic, the genetic
dictionary written according to the topographical
order of the chromosomes, evolution has written
another thousand after learning this dictionary,
on physiology, growth, ageing, immunity, etc., to

the topic of thought, which, doubtless, will never
be finished. It is even conceivable that one and
the same gene could acquire a different meaning
depending on its position in one or other genetic
sentence, just as a word in human language de-
pends on the context. Additionally, biological
functions have been invented as evolution has
become more complex. It is likely that genetic
combination was at the same time enriched by
new rules integrating earlier levels in as many
other Russian dolls of algorithms , placed inside
each other in a subtle hierarchy.
In sum, the syntax and style of the genetic lan-
guage has gradually been refined, and it remains
for us to discover its semantics and pragmatics. The
messages that determine eye colour, skin texture
or muscular mass are doubtless not the same as
those that induce the immune system, cellular
differentiation or cerebral wiring . Obviously,
many fundamental concepts of this language are
unknown. Even after sequencing is complete, there
will still be a lot to research to do and it will take
perhaps centuries of work to get things straight,
if we ever do. The question is that DNA is neither
an open topic nor a videotape.

FUTURE TRENDS

Now already numerous, wide-ranging and re-
warding, the interrelations between genetics and
computing will expand increasingly in the future.
The major trends that are likely to be the most
constructive are:

a. Biological computation: Under this label,

DNA computing deserves a mention. While
the early work on DNA computing dates
back to Adelman, there is still a long way
to go before we can build what is now be-
ing referred to as the chemical universal
Turing machine (CUMT). Its construction
would have an extraordinary effect on the
understanding of genetics and computer