[Nettime-bold] The Human Genome Race Considered as a High-Speed Data Dump

[Eugene Thacker <maldoror@eden.rutgers.edu>]

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The Human Genome Race Considered as a High-Speed Data Dump

Eugene Thacker



I. The Race

Celera - a latecomer to the race - got off to an energetic start. With a
souped-up high-octane arsenal of turbo-IT gene-machines at its side, it
looked as though this would be the quickest race of this type ever sanctioned.

The public consortium, however, was not so easily put off. Their old
school DNA bi-partisan tag-team strategy served them well, leading off
from multiple points in the U.S. and U.K., as well as Japan and Germany.

While Celera had the advantage coming into the race, its youth,
inexperience, and bold, brash style drew many criticisms. The public
consortium, on the other hand, was no stranger to the challenges of
extreme big science (or XBS).

Celera had, in many ways, fired the starting shot for the race when it
aggressively tapped the "enter" key on its sequencing computer in 1998.
With robust, lean vehicles from Perkin-Elmer, the data began flushing in
almost immediately. Patent, publish, and press enter again - in that order.

The public consortium quickly became aware of the sexy hum emanating
from Celera's gene machines. After doing some rush-delivery online
shopping, it too upgraded its tech, but without dropping its old school
appeal. Some of the researchers would actually teach and do research at
universities - a sign of by-gone days.

But the decider for the race was not Celera's new-kid-on-the-block
attitude, nor was it the public consortium's old-boys-will-prevail
assurance - it was software. The public consortium relied on the
pragmatics of the GigAssembler - 10,000 lines of code written in four
weeks by a grad student last summer: string them together & do a quick
taste-test. Celera stuck to its avant-garde guns by employing its
controversial Whole Genome Shotgun sequencing method (WGS): blow it to
pieces & then cluster them together freestyle.

Although both groups published their findings simultaneously, it appears
that Celera inched through the finish line at the last moment (though
frame-by-frame analysis has yet to confirm this - ftp clocks are being
checked at the servers of both Nature and Science magazines). Reports
from both groups state that this is only the end of the beginning.
Celera continues the race wireless - but that is another story.

(For more info, see "The Assassination of John Fitzgerald Kennedy
Considered as a Downhill Motor Race" by J.G. Ballard, and "The
Crucifixion Considered as an Uphill Bicycle Race" by Alfred Jarry.)


II. Reports

Critique functions the best, perhaps, when it does no work at all. That
is, when critique is embodied in the very same locus of its object, a
kind of discursive telescoping occurs, in which that which is implicit
is made explicit. The race to map the human genome has been such a
process. More than any other "big science" project, the mapping of the
genome has always partially un-done itself. It has played two roles:
that of a promotional campaign supporting a certain tradition of Western
science, and that of a highly unstable agenda for the production of a
hegemonic view of health, the body, and theories of biological "life."

As Mae-Wan Ho states in her recent critique of the genome
<http://www.i-sis.org>, a number of critics have long been suggesting
what scientists are only now beginning to consider, with the "hard
evidence" at hand: that genetic reductionism doesn't work, that "central
dogma" theories of any kind are highly limiting, and that "genes" (a
still relatively undefined category of biomolecule) may not be the
central and single most important component of molecular life. We can
look to Evelyn Fox Keller, Richard Lewontin, Susan Oyama, Donna Haraway,
Critical Art Ensemble and others for examples of such critiques. 

The recent reports on the human genome actually perform this - the
articles by Celera (in Science) and the International Human Genome
Consortium (in Nature) are filled with a rhetoric of surprise at the
findings: the number of "genes" is lower than expected, the number of
unique genes to humans is fewer than expected, the "coding" portion of
the genome is only about 1.5% of the total DNA, and vast regions of the
genome are filled with "deserts" of repetitive sequence whose function
is unknown.

Three perspectives here: What did they find? What do these findings
imply? What challenges do these findings put forth?


What did they find?: Both the public consortium and Celera came to
essentially the same conclusions in their initial analyses of the
genome. And both expressed "surprise" at their findings:

1. Number of genes: approx. 30,000-40,000. This is much less than
expected, with most estimates before this around 100,000 or more, and
many companies (mostly pharmaceutical or tech companies banking on the
high number of genes) are still in genomic denial. In addition, this is
not much more than bacteria or mice. In cross-species genome
comparisons, it was found that only some 300 genes are unique to human
beings compared with the mouse genome.

2. Large number of repetitive sequences. Again, a surprise finding, that
often large segments of DNA are repeat sequences, either inserted by
retroviruses, remnants of fossilized DNA, or sequences which play as yet
undetermined functions in gene expression and regulation. Jumping genes,
or "transposons," are beginning to be taken seriously as a primary
contingency in the dynamic aspects of the genome.

3. Modularity of genes in alternative splicing. Contrary to the
conservative one gene-one protein hypothesis, alternative splicing of
RNA from DNA (the "spliceosome") means that the combinatorial
possibilities of gene function increases greatly. In humans at least,
this means that one "gene" can have many different variations, and can
perform many different functions. There is no reason to think that it
would be different for proteins, or any other biomolecule in the cell.


What do these findings imply?: Reading the primary findings of the
genome analyses through the social and philosophical implications of
"surprise" discoveries, reveals several implicit critiques at work in
genomics specifically, and biotech generally:

1. The low number of genes is only surprising if it is assumed that the
quantity of genes is directly related to the complexity of the organism
or species. More genes would thus mean more complex organisms. In a
characteristically consumerist vein, the assumption here was that "more
is better."

2. The finding of repetitive sequences in the genome is only surprising
if it is assumed that each "gene" or DNA sequence has a unique function,
thereby making the genome original and unique. Repetition here is taken
to be synonymous with redundancy and a lack of novelty, while the icons
of American subjectivity - individuality, originality, novelty - are
assumed to reside in the very bodies of subjects, down to their
biomolecules. As bioinformatics is just beginning to demonstrate,
repetition can be a highly complex system for generating complexity (be
it ones and zeros or pairing of A-T and C-G).

3. The evidence of alternative splicing - modularity - in the production
of RNA is only surprising if it is assumed that one gene makes only one
protein. Despite the continual suggestion from both scientists and
cultural theorists, the old "central dogma" (DNA makes RNA makes protein
makes us) is still very much alive. A molecule with more than one
primary function, at one time, is beyond the scope of conventional
thinking in genetics and genomics.


What challenges do these findings put forth?: The initial genome
analyses make it all the more clear that different models of theorising
the genome and its significance are desperately needed, not only by
scientists, but by computer programmers, activists, artists, cultural
theorists, journalists, and policy makers. 

1. Informatic ontogeny: As Susan Oyama long ago theorised (_The Ontogeny
of Information_), the debates between organism and environment are
filled with assumptions about the pre-existence of both, often at the
expense of the productive, generative "interactionisms" that occur
within dynamic organisms and dynamic environments. Genes do not simply
already exist as fixed entities, and then interact with a fluctuating
environment. Information is actually generated within the dynamisms of
the organism-environment system - genetic information thus has an
ontogeny, a self-making property that emerges out of different
interactionisms. As Oyama states, "developmental information itself, in
other words, has a developmental history." Genetic information, then,
"neither preexists its operations nor arises from random order"; it is,
to paraphrase Gregory Bateson, a difference that makes a difference, and
therefore "its meaning is dependent on its actual functioning."

Several questions here: What if genetic "information" does not simply
pre-exist in each cell, waiting still and patiently to be mapped and
annotated? What if a "gene" describes not a specific string of DNA that
codes for the production of a protein, but a particular network of
operative biomolecules embedded within various contingencies, that have
a certain biochemical effect? There is already work being done in this
direction, but it remains marginal with respect to the genome.

2. Combinatorial complexity: The emergence of complexity out of
simplicity is the result of combinatorial variations. John von Neumann's
_Theory of the Automaton_ stated this very early on: complexity of
spatial patterning and temporal variation can arise out of the simplest
elements, be they binary bits or DNA molecules. A combinatorial
approaches relies on mathematics, stochastic approaches, etc., and
therefore also relies on computers: bioinformatics. This approach thus
demands that bioinformatics be more than merely a tool for reiterating
the central dogma (but with fancy molecular models); it demands that a
kind of computer science develop which takes into account the dynamic,
"wet" complexity of the molecular body.

The intersection of bio-science and computer science is intriguing; but
bioinformatics has a long way to go before it starts functioning as more
than a tool for big science. The interstitial fields of a-life,
bio-computing, and nanotech may provide a window to seeing past
bioinformatics as another lab tool. 

3. Systems approaches to molecular biology: The smaller number of genes
and their repetitions point to the obvious fact that a complex organism
such as the human somehow operates through apparent simplicity.
Gradually we're seeing the term "complex" and "complexity" seep into the
mainstream discourse surrounding genetics (both Venter and the New York
Times referred to the genome in this way). The discovery of DNA itself -
two pairs of two molecules - reiterates this theme of the simplicity of
beginning conditions, and their combinatorial complexity (see below).
Clearly what is needed is not a narrow, linear, production-oriented
approach (genes makes proteins make us), but a wider, more flexible,
more differentiated, systems approach to molecular biology. This would,
among other things, take into account the extra-genomic components
involved in cellular processes.

Writing about systems theory during the 1940s (_General System Theory_),
Ludwig von Bertalanffy states that "the living cell and organism is not
a static pattern or machine-like structure...It is a continuous
process...The organism's being an 'open system' is now acknowledged as
one of the most fundamental criteria of living systems..."


The initial reports from the human genome once again demonstrate that
what is needed - theoretically, technically, and politically - is
something very very simple: to think in more complex ways.
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Eugene Thacker
e: maldoror@eden.rutgers.edu
w: http://gsa.rutgers.edu/maldoror/index.html
Pgrm. in Comparative Literature, Rutgers Univ.
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CURRENT:
"Molecules That Matter: Nanomedicine & the
Advent of Programmable Matter" @ nettime: 
<http://www.nettime.org>.

"Regenerative Medicine: We Can Grow It For
You Wholesale" @ Machine Times (DEAF_00,
V2 book, http://www.v2.nl/deaf)

"The Post-Genomic Era Has Already Happened"
@ Biopolicy Journal <http://bioline.bdt.org.br/py>

"SF, Technoscience, Net.art: The Politics 
of Extrapolation" @ Art Journal 59:3 
<http://www.collegeart.org/caa/publications/AJ/artjournal.html>

"Point-and-Click Biology: Why Programming is
the Future of Biotech" @ MUTE (Issue 17 - archives
at http://www.metamute.com)
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also:
FAKESHOP <http://www.fakeshop.com>
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