Biology Reference
In-Depth Information
Physics served as the paradigm of such organization. But the physical
sciences also provided the model for the kinds of problems that comput-
ers were supposed to solve: those involving “large masses of data and
many complicated interrelating factors.” Many of the biomedical appli-
cations of computers that Ledley's volume explored treated biological
systems according to their physical and chemical bases. The examples
Ledley describes in his introduction include the numerical solution of
differential equations describing biological systems (including protein
structures, nerve fi ber conduction, muscle fi ber excitability, diffusion
through semipermeable membranes, metabolic reactions, blood fl ow),
simulations (Monte Carlo simulation of chemical reactions, enzyme
systems, cell division, genetics, self-organizing neural nets), statistical
analyses (medical records, experimental data, evaluation of new drugs,
data from electrocardiograms and electroencephalograms, photomicro-
graphic analysis); real-time experimental and clinical control (automatic
respirators, analysis of electrophoresis, diffusion, and ultracentrifuge
patterns, and counting of bacterial cultures) and medical diagnosis (in-
cluding medical records and distribution and communication of medical
knowledge).
24
Almost all the applications were either borrowed directly
from the physical sciences or depended on problems involving statistics
or large volumes of information.
25
For the most part, the mathematization and rationalization of biol-
ogy that Ledley and others believed was necessary for the “computeriza-
tion” of the life sciences did not eventuate.
26
By the late 1960s, however,
the invention of minicomputers and the general reduction in the costs
of computers allowed more biologists to experiment with their use.
27
At Stanford University, a small group of computer scientists and biolo-
gists led by Edward Feigenbaum and Joshua Lederberg began to take
advantage of these changes. After applying computers to the problem
of determining the structure of organic molecules, this group began to
extend their work into molecular biology.
28
In 1975, they created MOLGEN, or “Applications of Symbolic
Computation and Artifi cial Intelligence to Molecular Biology.” The
aim of this project was to combine expertise in molecular biology with
techniques from artifi cial intelligence to create “automated methods for
experimental assistance,” including the design of complicated experi-
mental plans and the analysis of nucleic acid sequences.
29
Lederberg and
Feigenbaum initially conceived MOLGEN as an artifi cial intelligence
(AI) project for molecular biology. MOLGEN included a “knowledge
base” compiled by expert molecular biologists and containing “declara-
tive and procedural information about structures, laboratory condi-
