Biology Reference
In-Depth Information
of interest. The representatives of the biology institutes make
presentations demonstrating the speed and cost-effectiveness of
their past sequencing efforts—they want to secure NIH grant
money for genome sequencing. After the presentations, argu-
ments revolve around how best to serve biology by selecting
species that will provide appropriate comparisons with known
genomes. The discussion comes to a close at 5:30 p.m., and the
academic and institute scientists return to their homes; it is now
up to the NIH employees to make the fi nal decisions about what
to sequence and how to allocate their funds.
Scene 5—Back to the lab
: Finally, we enter a space that looks
more like a biology lab—pipettes, test tubes, bottles of reagents,
and discarded rubber gloves are scattered around the bench
spaces. One side of the room is dominated by a particularly
large machine: it is a Solexa sequencing machine, a large rectan-
gular box with a transparent panel at the front. The lab workers
spend several days preparing DNA samples for this machine,
making sure the samples are buffered with the right concoc-
tion of chemicals for the sequencing reactions to take place. The
samples are inserted into the panel on the front. A “run” of the
machine takes several days, so workers go about other work,
often preparing more samples for the next run. The results—
the data, the sequences—are automatically sent to a computer
connected to the sequencing machine. One of the workers now
begins to analyze the data. The sequences from the machine
must be rigorously compared with sequences contained in on-
line databases. The worker performs a search to compare the se-
quences with those in GenBank; if the sequences return positive
hits against human DNA, then it is likely that the samples were
contaminated by DNA from lab workers (since in this case, the
sequences are known to come from mouse cells). Next, a more
sophisticated set of searches is performed against a range of
other databases in order to identify the particular genes and
exact genomic loci from which the samples have come.
This diversity of practices makes clear that recent transformations of bi-
ology are not merely from noncomputer to computer work. Big Biology
now requires large amounts of funding, big laboratories, interdisciplin-
ary teams (computer scientists, mathematicians, physicists, chemists,
technicians, and so on), and the expertise to manage all of these. Biolo-
