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In
retrospect, the marriage of genetics and computers
was pre-ordained. After all, biotechnology is
based on the genetic building-blocks of life—in
short, on nature's huge encyclopedia of information.
And hidden in the vast sequences of A (adenine),
G (guanosine), C (cytosine) and T (thymine)
that spell out the genetic messages—ie,
genes—are functions that take an input
and yield an output, much as computer programs
do. Yet the computerisation of genetics on such
a grand scale would not have occurred without
the confluence of three things: the invention
of DNA microarrays and
high-throughput screening; the sequencing of
the human genome; and a dramatic increase in
computing power.
More
commonly known as “gene chips”,
microarrays are to the genetic revolution of
today what microprocessors were to the computer
revolution a quarter of a century ago. They
turn the once arduous task of screening genetic
information into an automatic routine that exploits
the tendency for the molecule that carries the
template for making the protein, messenger-ribonucleic
acid (m-RNA), to bind to
the DNA that produces it.
Gene chips contain thousands of probes, each
imbued with a different nucleic acid from known
(and unknown) genes to bind with m-RNA.
The resulting bonds fluoresce under different
colours of laser light, showing which genes
are present. Microarrays measure the incidence
of genes (leading to the gene “sequence”)
and their abundance (the “expression”).
In
just a few years, gene chips have gone from
experimental novelties to tools of the trade.
A single GeneChip from Affymetrix, the leading
maker of microarrays based in Santa Clara, California,
now has more than 500,000 interrogation points.
(For his invention of the gene chip, Affymetrix's
Stephen Foder won one of The Economist's
Innovation Awards for 2002.) With each successive
generation, the number of probes on a gene chip
has multiplied as fast as transistors have multiplied
on silicon chips. And with each new generation
has come added capabilities.
The
sequencing of the human genome in late 2000
gave biotechnology the biggest boost in its
30-year history. But although the genome sequence
has allowed more intelligent questions to be
asked, it has also made biologists painfully
aware of how many remain to be answered. The
genome project has made biologists appreciate
the importance of “single nucleotide polymorphism”
(SNP)—minor variations
in DNA that define differences
among people, predispose a person to disease,
and influence a patient's response to a drug.
And, with the genetic make-up of humans broadly
known, it is now possible (at least in theory)
to build microarrays that can target individual
SNP variations, as well
as making deeper comparisons across the genome—all
in the hope of finding the obscure roots of
many diseases.
The
sequencing has also paved the way for the new
and more complex field of proteomics, which
aims to understand how long chains of protein
molecules fold themselves up into three-dimensional
structures. Tracing the few thousandths of a
second during which the folding takes place
is the biggest technical challenge the computer
industry has ever faced—and the ultimate
goal of the largest and most powerful computer
ever imagined, IBM's petaflop
Blue Gene. The prize may be knowledge of how
to fashion molecular keys capable of picking
the lock of disease-causing proteins.
The
third ingredient—the dramatic rise in
computing power—stems from the way that
the latest Pentium and PowerPC
microprocessors pack the punch of a supercomputer
of little more than a decade ago. Thanks to
Moore's law (which predicted, with remarkable
consistency over the past three decades, that
the processing power of microchips will double
every 18 months), engineers and scientists now
have access to unprecedented computing power
on the cheap. With that has come the advent
of “grid computing”, in which swarms
of lowly PCs, idling between
tasks, band together to form a number-crunching
mesh equivalent to a powerful supercomputer
but at a fraction of the price. Meanwhile, the
cost of storing data has continued to fall,
and managing it has become easier thanks to
high-speed networking and smarter forms of storage. |
