行政院國家科學委員會專題研究計畫 成果報告
利用「行為資料採礦」探索生物系統的網路基序
計畫類別： 個別型計畫
計畫編號： NSC93-2213-E-110-029-
執行期間： 93 年 08 月 01 日至 94 年 07 月 31 日
執行單位： 國立中山大學資訊管理學系(所)
計畫主持人： 鄭炳強
共同主持人： 鄒文雄
計畫參與人員： 王修齊、侯雍聰
報告類型： 精簡報告
處理方式： 本計畫可公開查詢
中 華 民 國 94 年 10 月 26 日
Discovering network motifs by behavior data mining
NSC 93-2213-E-110-029
93
8
1
94
7
31
Email: [email protected]
(motif)
Abstract
In the era of post-genome, scientists are
aware that the study of biological systems has
to be upgraded from molecular level to
network level. Past research shows that there
are some highly re-occurrence subnets exist in
the biological networks of Saccharomyces
Cerevisiae and E. Coli, which are called
network motifs. It is guessed that they are
reserved in the evolution because of their
specific functions which are useful to a system.
Current approaches to discover network motifs
all assume prior knowledge of links between
molecular nodes, and justify the existence by
some statistic techniques. Since a motif has its
dynamics behavior model, it will be interesting
if one could discover it from the existing
experimental behavior data by some data
mining techniques. We made this attempt in
this project and discussed what we found and
encountered problems.
Keywords: Network Motif, Behavior
Datamining, Sequence Alignment
(network motif)
[3]
(randomized network)
[3, 5] Milo
Lee[4]
6
1.Autoregulatory 2. Feed-forward loop 3.
Multi-component loop 4.Single input module 5.
Multi-input module 6.Regulator cascade (
1)
1. Feed-forward
loop(FFL)
module
(threshold)
FFL
[3]
(
)
1. Examples of network motifs in the yeast
regulatory network
Lee, T., et.al.,2002[4]
Alon
FFL [13 14]
(model)
Alon
DNA
Microarray[12]
mRNA
Microarray
[10
11]
-
[2
13
14]
[6
7
8
9]
g
13
7
mRNA
g
24
8
g
1g
2time series
[16]
T
={X
1,X
2, K,
n}
(real number)
X
ii
T
jj
mRNA
10
n = 10
T
1= {X
1,X
2, K,
n} T
2= {Y
1,Y
2, K,
n}
f(T
1,T
2)
= [
X
1-Y
1 2+(X
2-Y
2)
2+
K+X
n-Y
n)]
1/2f(T
1,T
2)
(
)
T
1, T
2f(T
1,T
2)
Euclidean Distance
Microarray
mRNA
[1]
metric
Microarray
X
iY
i 1Liping Ji
[11]
- 1(
) -1(
)
0(
)
m
n
m n
T
ijT
iji
j
mRNA(
)
1 <= i <= m
0 <= j <= n
T
ijT
i(j+1)T
ij(1)
T
ij0
T
ij= (T
i(j+1)- T
ij) /
T
ij(2)
T
ij= 0
T
i(j+1)> 0
T
ij= 1
(3)
T
ij= 0
T
i(j+1)< 0
T
ij= -1
(4)
T
ij= 0
T
i(j+1)= 0
T
ij= 0
Normalization Threshold(t) (
1)
T
ijT
ij(1)
T
ijt
T
ij= 1
(2)
T
ij-t
T
ij= -1
(3)
T
ij= 0
2(pattern)
metric
Hamming Distance:
s t |s| = |t| s
t
Hamming Distance
H(s t) =
1( , )
n i i imismatch s t
s
it
i( , )
i imismatch s t =1
mismatch s t =0
( , )
i iH
____________________________________ 1 2 : ababccglobal
H(s
1s
2)
(local)
H
abababaaaabababa
bcbcbcaaaababab
n
T
p
w; w < n 1 <= p <= n-w+1
C
p=
{X
p,X
p+1, K
p+w-1}
p
w-1
T
aabb bbcc
aabbcc
3
T
i:{-1,0,1}
q
3
qq-cluster[11]
:(0 0)
(0 1)
(1 0)
(1 1)
2
H = 0
q
q-clusters
FFL
[14]
FFL
X
Y
Y
Z
X
)
Y
Z
X
Y Z
q-cluster :
0 1 1 1 1
(3
2) (5
3) (7
4)
0 -1 -1 -1 -1
(3
7) (5
8) (7
8)
3
5
7
____________________________________ 3 sliding windowFFL
5
false-
positive
Microarray array
[13
14]
Microarray mRNA
[12]
(
FFL-Motif)
[13
14]
Microarray
(
mRNA
false- positive
[12]
Microarray
10
[12]
mRNA
(post-translational modification)
Microarray mRNA
[13
14]
[15]
Microarray
(
:
-DNA
-knockout
)
Microarray
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