Biology Reference
In-Depth Information
things change” (383). Nanoengineers are not using visualization as a tool for
fi nding some sort of “truth to nature,” but as a tool to make nature. What is
going on in bioinformatics is different—biologists are not trying to construct
or manipulate external objects using images. Bioinformatic images are not
attempts to create “objective” representations of biology, but rather attempts
to address the problem of managing the vast amounts of data and numbers of
computations that make it practically impossible to fully “check up” on a com-
puter's result. Images are manipulated not as means of manipulating nature,
but as means of analyzing and understanding it.
3. The work of Lily Kay (
Who Wrote the Book of Life?
) and Evelyn Fox
Keller (
Making Sense of Life
) has sensitized historians to the importance of
textual metaphors in molecular biology. But other nontextual metaphors are
now becoming increasingly important. For one interesting take, see the work
of Natasha Myers on kinaesthetic metaphors in biology (Myers, “Modeling
Proteins”).
4. The same is true in other biological fi elds such as artifi cial life (see Helm-
reich,
Silicon Second Nature
).
5. The MAC in Project MAC originally stood for Mathematics and Com-
putation; it was later renamed Multiple Access Computer, then Machine Aided
Cognitions or Man and Computer.
6. Levinthal et al., “Computer Graphics.”
7. Francoeur and Segal, “From Model Kits.” See also Francoeur, “The For-
gotten Tool,” and Francoeur, “Cyrus Levinthal.”
8. Ledley,
Use of Computers
.
9. On the signifi cance of scientifi c atlases, see Daston and Galison, “Image
of Objectivity.” Daston and Galison argue that atlases became means to me-
chanically and objectively represent and order the phenomena of nature.
10. See Stevens, “Coding Sequences.”
11. The fi rst description of this method can be found in Gibbs and Mc-
Intyre, “The Diagram.” For a detailed history of dot plots, see Wilkinson,
“Dot Plots.”
12. See Stevens, “Coding Sequences,” and Needleman and Wunsch, “A
General Method.”
13. For instance, a bioinformatics textbook describes the algorithm in
visual language: “The best alignment is found by fi nding the highest-scoring
position in the graph, and then tracing back through the graph through the
path that generated the highest-scoring positions.” Mount,
Bioinformatics
, 12.
14. To explain in a little more detail, Needleman-Wunsch works by identi-
fying small matching regions of nucleotides (or residues in the case of proteins)
and then trying to extend those alignments outward. Working from left to
right, the grid is populated with scores according to either fi nding matches
or inserting gaps in the sequence. When all the squares in the grid have been
computed, the highest score appears somewhere at the bottom right; it is then
possible to read-off the best alignment from right to left by “tracing back”
what sequence of moves resulted in this high score.
15. National Academy of Sciences et al.,
Information Technology
, 1.
16. McCormick et al., “Visualization,” 7.
