Network Motifs: Simple
Building Blocks of Complex Networks,, Science
Excerpts: Complex networks are
studied across many fields of science. To uncover their structural
design principles, we defined "network motifs," patterns of
interconnections occurring in complex networks at numbers that are
significantly higher than those in randomized networks. We found
such motifs in networks from biochemistry, neurobiology, ecology,
and engineering. (¡K) Similar motifs were found in networks
that perform information processing, even though they describe
elements as different as biomolecules within a cell and synaptic
connections between neurons in Caenorhabditis elegans. Motifs may
thus define universal classes of networks.
Similar Patterns In Genes,
Brains, Feeding, UPI Science News
Excerpts: "We start with a
network -- a list of elements and their connections," he said. "We
then count how many times different patterns appear in this
network. To understand which of the many patterns that occur are
significant and potentially important, we compare the network to a
large set of randomized networks. (...) In each of the randomized
networks we again count the number of appearances of the different
patterns."
After a while, the computer program reveals some patterns
["motifs", Ed.] occur much more often than they would at
random.
Transcriptional Regulatory
Networks in Saccharomyces cerevisiae, Science
Excerpt: We have determined how most
of the transcriptional regulators encoded in the eukaryote
Saccharomyces cerevisiae associate with genes across the genome in
living cells. (¡K)We use this information to identify network
motifs, the simplest units of network architecture, and
demonstrate that an automated process can use motifs to assemble a
transcriptional regulatory network structure. Our results reveal
that eukaryotic cellular functions are highly connected through
networks of transcriptional regulators that regulate other
transcriptional regulators.
- Transcriptional
Regulatory Networks in Saccharomyces
cerevisiae, Lee, Tong
Ihn, Rinaldi, Nicola J., Robert, Francois, Odom,
Duncan T., Bar-Joseph, Ziv, Gerber, Georg K., Hannett,
Nancy M., Harbison, Christopher T., Thompson, Craig
M., Simon, Itamar, Zeitlinger, Julia, Jennings, Ezra
G., Murray, Heather L., Gordon, D. Benjamin, Ren,
Bing, Wyrick, John J., Tagne, Jean-Bosco, Volkert,
Thomas L., Fraenkel, Ernest, Gifford, David K., Young,
Richard A., Science 2002 298: 799-804
Growing And Navigating The
Small World Web By Local Content, PNAS
Excerpts: Can we model the scale-free distribution
of Web hypertext degree under realistic assumptions about the
behavior of page authors? (...) receiving much attention due to
their potential impact for understanding the structure of the Web
(...). Here I investigate the connection between the linkage and
content topology of Web pages. The relationship between a
text-induced distance metric and a link-based neighborhood
probability distribution displays a phase transition between a
region where linkage is not determined by content and one where
linkage decays according to a power law.