Nature Genetics
22, 281 - 285 (1999)
doi:10.1038/10343
Systematic determination of genetic network architectureSaeed Tavazoie1, Jason D. Hughes1, 2, Michael J. Campbell3, Raymond J. Cho4
& George M. Church11
Department of Genetics, Harvard Medical School,
200 Longwood Ave, Boston, Massachusetts
02115, USA.
2
Graduate Program in Biophysics, 200 Longwood
Ave, Harvard University, Boston, Massachusetts
02115, USA.
3
Molecular Applications Group, 607 Hansen
Way, Building One, Palo Alto, California
94303-1110, USA.
4
Department of Genetics, B400 Beckman Center,
279 Campus Drive, Stanford Medical Center, Palo Alto
, California 94304, USA.
Correspondence should be addressed to George M. Church church@salt2.med.harvard.eduTechnologies to measure whole-genome mRNA abundances1,
2,
3
and methods to organize and display such data4,
5,
6,
7,
8,
9,
10
are emerging as valuable tools for systems-level exploration of transcriptional
regulatory networks. For instance, it has been shown that mRNA data from 118
genes, measured at several time points in the developing hindbrain of mice,
can be hierarchically clustered into various patterns (or 'waves') whose members
tend to participate in common processes5. We have previously
shown that hierarchical clustering can group together genes whose cis-regulatory
elements are bound by the same proteins in vivo
6. Hierarchical
clustering has also been used to organize genes into hierarchical dendograms
on the basis of their expression across multiple growth conditions7.
The application of Fourier analysis to synchronized yeast mRNA expression
data has identified cell-cycle periodic genes, many of which have expected
cis-regulatory elements8. Here we apply a systematic set
of statistical algorithms, based on whole-genome mRNA data, partitional clustering
and motif discovery, to identify transcriptional regulatory sub-networks in
yeast—without any a priori knowledge of their structure or any assumptions
about their dynamics. This approach uncovered new regulons (sets of co-regulated
genes) and their putative cis-regulatory elements. We used statistical
characterization of known regulons and motifs to derive criteria by which
we infer the biological significance of newly discovered regulons and motifs.
Our approach holds promise for the rapid elucidation of genetic network architecture
in sequenced organisms in which little biology is known.
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