Biomedical Engineering Reference
In-Depth Information
groups, or the whole world. In most of these environments switching between
completely private and public is a matter of pressing a button, making it pos-
sible to author in private but easily publish to the Web. Authoring and securing
criticism prior to “publication” are both much easier and potentially much
more effective. However the potential of these tools remains unexploited
while most authors prefer to e-mail around Word documents.
In the future wikis and other social software are likely to continue their
growth and prominence as knowledge-sharing environments with the software
platforms continuing to expand in functionality based on the needs of the
user organizations. Google SideWiki (http://www.google.com/sidewiki/intl/en/
index.html) already allows anyone to leave comments about pages as they surf
the Web, thereby further enabling the community to participate in wiki'ing the
Web. The challenges for wikis will be whether they can be seen as nearly
equivalent to traditional peer-reviewed publishing to gain further acceptance
from the scientifi c community. Until their credibility increases, individuals will
be less motivated to participate compared with other modes of communica-
tion. Will wikis change the world? They already are and could be exploited
further to impact biomedical research.
28.6
COLLABORATIVE SYSTEMS BIOLOGY
In a similar way to wikis (described above), we presently take for granted a
systems-level understanding of the linked networks of multiple interacting
genes, gene products, and metabolic processes that determine phenotype.
Systems biology is considered an interdisciplinary methodology incorporating
collaborations between experimental biology, physiology, physics, engineering,
mathematics, and computer science. Systems biology emphasizes combining
high-quality, quantitative data from multiple levels with computational model-
ing to develop mechanism-based models of how networks of individual genes
and proteins interact. For many years the functional organization of a biologi-
cal system was described in terms of pathways which were relatively small
linear chains of biochemical reactions or signaling interactions. In recent years,
as biology has used high-throughput methods for determining protein-protein
interactions, it has also required the development of pathway databases and
natural language processing algorithms for automatic extraction of pathway
information. Now we realize that biology is enormously complex and molecu-
lar processes can be linked to very large, highly interconnected networks [68].
We have seen the availability of software for visualizing complex gene net-
works become very commonplace, and this, in many ways, has been the under-
pinning of research on systems biology [41-49]. For example, combining
comprehensive databases, powerful analytical and network building tools have
resulted in the development of commercially available integrated high-
throughput data - mining suites like Pathway Assist ™ (Ingenuity), PathArt ™
(Jubilant Biosys), Pathways Studio™ (Ariadne), MetaCore™, MetaDrug™
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