Bioinformatics Vol. 18 no. 11 2002
Pages 1486-1493
© 2002 Oxford University Press
A duplication growth model of gene expression networks
Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA
Received on December 13, 2001
; revised on February 8, 2002 and April 29, 2002
; accepted on May 3, 2002
Motivation: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network.
Results: Several whole-genome expression time series data sets from yeast
microarray experiments were analyzed using a Markov-modeling
method (Dewey and Galas, Func. Integr.
Genomics, 1, 269–278, 2001) to
infer an approximation to the underlying genetic network. We
found that the global statistical properties of all the
resulting networks are similar. The overall structure of these
biological networks is distinctly different from that of other
recently studied networks such as the Internet or social
networks. These biological networks show hierarchical, hub-like
structures that have some properties similar to a class of
graphs known as small world graphs. Small world networks
exhibit local cliquishness while exhibiting strong global
connectivity. In addition to the small world properties, the
biological networks show a power law or scale free distribution
of connectivities. An inverse power law, N(k)
k-3/2,
for the number of vertices (genes) with k connections was
observed for three different data sets from yeast. We propose
network growth models based on gene duplication events.
Simulations of these models yield networks with the same
combination of global graphical properties that we inferred from
the expression data.
Contact: Ashish_Bhan{at}kgi.edu David_Galas{at}kgi.edu Greg_Dewey{at}kgi.edu
Supplementary Information: http://www.kgi.edu/html/noncore/faculty/dewey/bioinf.pdf
* To whom correspondence should be addressed.
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