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Oct 1, 2013 (4 years and 11 days ago)

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A GUIDED SAMPLING ALGORITHM FOR IDENTIFYING NETWORK
MOTIFS IN A TRANSCRIPTION REGULATORY NETWORK


Raymond
Wan
*
,
Nelson
Hayes
*
,
Susumu
Goto,
Hiroshi
Mamitsuka

{rwan,nelson,goto,mami}@bic.kyoto
-
u.ac.jp


Bioinformat
i
cs Center, Institute for Chemical Research,

Kyoto University, Gokasho, Uji,
611
-
0011, Japan


The identification of network motifs is an important problem in bioinformatics due to its
complexity and application to other areas. N
etwork motifs are statistically
significant
subgraphs when compared to
a random network
[Milo et al., 2002]
.
Milo et al.

showed
that the technique used for transcription regulatory networks can also be applied to
artificial networks such as electronic circuits and links between Web documents. One
refinement to their brute
-
f
orce technique involved random sampling

[Kashtan et al.,
2004]
.


In this poster, we describe some work
-
in
-
progress which is an extension to the random
sampling approach. Determining whether or not a network motif is statistically

significant depends on the

number of samples taken. However, the fact that graphs such
as transcription regulatory networks contain local clusters
[Artzy
-
Randrup et al., 2004]
,
has motivated us to investigate the possibility of guiding the sampling process.
Essentially, not all v
ertices in the graph are equal. We apply a scoring mechanism to
each vertex based on the number of directed edges it and its neighbors contain. Then
, we
group the vertices into
n

buckets based on the distribution of the scores. By sa
mpling the
same prop
ortion from each bucket, there is an approximately equal probability of
sampling vertices from regions of high and low connectivity. Preliminary experiments
with the transcription regulatory network of
E.

coli

ha
ve

shown that the feed
-
forward
loop identif
ied by others
[Milo et al., 2002]

could be

located by our method usin
g
roughly 25
% of the vertices. In the future, we plan to expand our experiments to other
data sets and explore other vertex scoring mechanisms in order to refine our technique.


Referenc
es


Y.

Artzy
-
Randrup, S.

J. Fleishman, N.

Ben
-
Tal, and L.

Stone.

Comment on `
N
etwork
Motifs: Simple
Building Blocks of C
omplex

N
etworks' and `
S
uperfamilies of

Evolved and Designed N
etworks'
.

Science
,
305(5687):
1107c, 2004
.


N. Kashtan, S. Itzkovitz, R. Mi
lo, and U. Alon. Efficient
S
ampling
A
lgorithm for
E
stima
ting
S
ubgraph
C
oncentrations and
D
etecting
N
etwork
M
otifs.
Bioinformatics
, 20(11):
1746
-
1758, 2004.


R. Milo, S. Shen
-
Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network
M
otifs
: S
impl
e
B
uilding

B
locks of
C
omplex
N
etworks.
Science
, 298(5994):
824
-
827, 2002.






*

These authors contributed equally to this work.