Biomedical Engineering Reference
In-Depth Information
Endnote
The history of innovation and development in computing and molecular biology is much more
complicated than that suggested by the timeline in
Figure 1-2
or by the Central Dogma. The flow of
information through replication, transcription, and translation is more involved than described here,
and there are unknowns and exceptions to most of the theories put forth by investigators. For
example, the Central Dogma is only partially correct, in that the flow of information isn't
unidirectional as Watson initially proposed. In contrast to the Central Dogma, information can flow
from external sources into the genome. For example, retroviruses or RNA-based viruses such as HIV
copy their genetic information into the host cell's DNA, where the cell's machinery obediently
duplicates the retrovirus.
In addition, there are many more unknowns than the role of introns and other apparently non-coding
DNA in the chromosomes. Many of the proteins in the human proteome haven't been cataloged, and
the roles of those that have been cataloged are poorly understood. Similarly, the processes of
replication, transcription, and translation are exceedingly complex, involving hundreds of thousands
of operations mediated by hundreds of factors, only a few of which are understood. Furthermore, the
information-transfer process described by the Central Dogma differs somewhat from that used by
mitochondria and some microorganisms.
What's more, it's possible that the source of much of the work in bioinformatics—the human
genome—is inherently biased. Because much of the sequence data is derive from analysis of Craig
Venter's DNA, with minimal contributions from five other donors, the data necessarily reflect Venter's
genotype. Although it was recognized early on that his DNA carries a variant gene associated with
abnormal fat metabolism and Alzheimer's disease, other variants carried by Venter that have not yet
been studied may be considered normal for the human genome until more research is performed.
Undoubtedly, over the next decade, when scientists finally finish and verify the genome sequence,
other discoveries will be made as well. For example, it's unclear what the sequences in the
centromeres will reveal, especially because the sequences in those regions of each chromosome have
been resistant to sequencing techniques used on the other parts of the chromosomes.
Similarly, developments in computer science have not been as straightforward as suggested by the
timeline. For example, the promise of AI, the darling of the computer science community throughout
the 1980s, never materialized. After the massive military funding for language translation
evaporated, the few companies that attempted to survive in the commercial world folded. Even the
notable academic systems, such as MYCIN—the first rule-based expert system in medicine—were
never put to practical use. What survives today are the various pattern-recognition methods and
object-oriented programming techniques that are invaluable in genome and proteome research.
The timeline offered here also glosses over much of the human struggle involved in the discoveries
and triumphs in both molecular biology and computing. For example, James Watson was initially in
charge of the Human Genome Project, but resigned after only a few years because of a feud with the
director of the National Institutes of Health over gene patenting. His successor, Francis Collins was
then embroiled in competition with Craig Venter's private research institute over methodology.
Although Venter prevailed and won the race to decode the majority of what is currently understood to
be the human genome, the commercial viability of his company is less certain. Similarly, there is
turmoil—and millions of dollars at stake—over determining who should be credited with the basic
sequencing technique.
Just as the hype of what AI was supposed to deliver served to kill the industry for many years, many
of the favored genomics research firms have performed less profitably than expected on Wall Street.
Some genetically engineered drugs have not taken off as expected, and companies such as Genetech
have been forced to turn to modifications of conventional pharmaceuticals to stay in business.
When exploring the computational methods described in this topic, the reader is encouraged to apply
basic business metrics to the information. For example, what is the added value of each step in the
