Transcript Slide 1

Lecture7
Introduction to signaling pathways
Reverse Engineering of biological networks
Metabolomics approach for determining
growth-specific metabolites based on FT-ICR-MS
Self organizing mapping(SOM)
Introduction to signaling pathways
Signaling networks involves the transduction of “signal”
usually from outside to the inside of the cell
On molecular level signaling involves the same type of
processes as metabolism such as production and degradation
of substances, molecular modifications (mainly
phosphorylation but also methylation and acetylation) and
activation or inhibition of reactions.
But signaling pathways serve for information processing or
transfer of information while metabolism provide mainly
mass transfer
Introduction to signaling pathways
Signal transduction often involves:
•The binding of a ligand to an extracellular receptor
•The subsequent phosphorylation of an intra cellular
enzyme
•Amplification and transfer of the signal
•The resultant change in the cellular function e.g. increase
/decrease in the expression of a gene
Signaling paradiam
Usually a signaling network has three principal parts:
Events around the membrane
Reactions that link sub-membrane events to the nucleus
Events that leads to transcription
Source: Systems biology in practice by E. klipp et. al.
Schematic representation of receptor activation
Source: Systems biology in practice by E. klipp et. al.
Steroids
Not always a receptor exists at
the membrane for example the
steroid receptors.
Sterol lipids include hormones
such as cortisol, estrogen,
testosteron and calcitriol.
These steroids simply cross the
membrane of the target cell and
then bound the intracellular
receptor which results in the
release of the inhibitory
molecule from the receptor.
The receptor then traverses the
nuclear membrane and binds to
its site on the DNA to trigger the
transcription of the target gene.
Source: Systems biology by Bernhard O. Palsson
G-protein signaling
G-protein coupled receptor (GPCR)
represents important components of
signal transduction network
This class of receptor comprises 5% of
the genes in C. elegans
The G-protein complex consists of
three subunits (α, β and λ) and in its
inactive state bound to guanosine
diphosphate(GDP)
When a ligand binds to the GPCR, the
G-protein exchanges its GDP for a
guanosine trihosphate(GTP)
This exchange leads to the dissociation
of the G-protein from the receptor and
its split into a βλ complex and a GTPbound α subunit which is its active
state initiating other downstream
processes
Source: Systems biology by Bernhard O. Palsson
G-protein signaling model
Source: Systems biology in practice by E. klipp et. al.
G-protein signaling model
Time course of G protein activation. The total number of
molecules is 10000. The concentration of GDP-bound Gα is low
for the whole period due to its fast complex formation with the
heterodimer Gβλ
Source: Systems biology in practice by E. klipp et. al.
The JAK-STAT network
The JAK-STAT signaling
system is an important twostep process that is involved
in multiple cellular functions
including cell growth and
inflammatory response
A cell surface receptor often dimerizes upon binding to a cytokine
The monomeric form of the receptor is associated with a kinase called
JAK
When the receptor dimerizes the JAKs induce phosphorylation of
themselves and the receptor which is the active state of the receptor.
The active complex phosphorylates the STAT(signal transducer and
activator of transcription) molecules
STAT molecules then dimerizes, go to nucleus and trigger transcription
Source: Systems biology in practice by E. klipp et. al.
Schematic representation of the MAP kinase cascade. An
upstream signal causes phosphorylation of the MAPKKK. The
phosphorylation of the MAPKKK in turn phosphorylates the
protein at the next level. Dephosphorylation is assumed to occur
continuously by phosphatases or autodephosphorylation
Source: Systems biology in practice by E. klipp et. al.
Signaling pathways in Baker’s yeast
HOG pathway activated by osmotic shock, pheromone pathway
activated by pheromones from cells of opposite mating type and
pseudohyphal growth pathway stimulated by starvation condition
A MAP kinase cascade is a particular part of many signalling
pathways . In this figure its components are indicated by bold
border Source: Systems biology in practice by E. klipp et. al.
Reverse Engineering of biological networks
The task of reverse engineering of a genetic network is the
reconstruction of the interactions among biological entities (
genes, proteins, metabolites etc.) in a qualitative way from
experimental data using algorithm that weight the nature of
the possible interactions with numerical values.
In forward modeling network is constructed with known
interactions and subsequently its topological and other
properties are analyzed
In reverse engineering the network is estimated from
experimental data and then it is used for other predictions
Reverse Engineering of gene regulatory network
By clustering the gene expression data, we can determine coexpressed genes.
Co-expressed genes might have similar regulatory characteristics
but it is not possible to get the information about the nature of
the regulation.
Here we discuss a reverse engineering method of estimating
regulatory relation between genes based on gene expression
data from the following paper:
Reverse engineering gene networks using singular value
decomposition and robust regression
M. K. Stephen Yeung, Jesper Tegne´ r†, and James J. Collins‡
Proc. Natl. Acad. Sci. USA 99:6163-6168
Reverse Engineering of gene regulatory network
It is assumed that the dynamics i.e. the rate of change of a geneproduct’s abundance is a function of the abundance of all other
genes in the network.
For all N genes the system of equations are as follows:
In Vector notation
Where f(X) is a vector valued function
Reverse Engineering of gene regulatory network
Under linear assumption i.e. has linear relation with Xi s we
can write
Here Aij is the coupling parameter that represents the
influence of Xj on the expression rate of Xi . In other words Aij
represents a network showing the regulatory relation among
the genes.
Target of reverse engineering is to determine A. Solving A
requires a large number of measurements of and X
Reverse Engineering of gene regulatory network
Measurement of
several ways.
is difficult and hence can be estimated in
First, if time series data can be obtained then can be
approximated by using the profiles of the expression values for
fixed time intervals
Alternatively a cellular system at steady state can be perturbed
by external stimulation and then
can be determined by
comparing the gene expression in the perturbed cellular
population and the unperturbed reference population.
Reverse Engineering of gene regulatory network
Now using any method if we can produce matrices
then we can write
Or,
and
(if external perturbation is used)
Here BNxM is the matrix representing the effect of
perturbation
The goal of reverse engineering is to use the measured data
B, X, and to deduce A i.e. the connectivity matrix of the
regulatory relation among the genes.
Reverse Engineering of gene regulatory network
By taking transpose the system can be rewritten as
A is the unknown. If M =N and X is full-ranked, we can simply
invert the matrix X to find A. However, typically M<<N mainly
because of the high cost of perturbations and measurements.
We therefore have an underdetermined problem.
Underdetermined problem means the number of linearly
independent equations is less than the number of unknown
variables. Therefore there is no unique solution One way to
get around this is to use SVD to decompose XT into
Reverse Engineering of gene regulatory network
where U and V are each orthogonal which means:
with I being the identity matrix, and W is diagonal:
Without loss of generality, we may assume that all nonzero
elements of wk are listed at the end, i.e., w1, w2, . . . , wL =0
and wL+1, wL+2,. . . , wN≠0, where L :=dim(ker(XT)). Then one
particular solution for A is:
Reverse Engineering of gene regulatory network
the general solution is given by the affine space
with C = (cij)N×N, where cij is zero if j >L and is otherwise an
arbitrary scalar coefficient. This family of solutions in Eq. 3
represents all the possible networks that are consistent with the
microarray data. Among these solutions, the particular solution
A0 is the one with the smallest L2 norm.
Now, the question is which one of the solutions of equation 3 is
the best.
Reverse Engineering of gene regulatory network
In such cases, we may rely on insights provided by earlier
works on gene regulatory networks and bioinformatics
databases, which suggest that naturally occurring gene
networks are sparse, i.e., generally each gene interacts with
only a small percentage of all the genes in the entire genome.
Imposing sparseness on the family of solutions given by Eq. 3
means that we need to choose the coefficients cij to maximize
the number of zero entries in A. This is a nontrivial problem.
Reverse Engineering of gene regulatory network
The task is equivalent to the problem of finding the exact-fit
plane in robust statistics, where we try to fit a hyperplane to a
set of points containing a few outliers.
Here they have chosen L1 regression where the figure of merit
is the minimization of the sum of the absolute values of the
errors, for its efficiency.
In short, this method of reverse engineering can produce
multiple solutions (gene networks) that are consistent with a
given microarray data. This paper says among them the
sparsest one is the best solution and used L1 regression to
detect the best solution.
Metabolomics approach for determining growth-specific
metabolites based on FT-ICR-MS
24
[1] Metabolomics
Tissue Samples
MS
Species
Metabolite information
Molecular weight and formula
Fragmentation Pattern
Experimental Information
Species
Metabolite 1
Species-Metabolite relation DB
Metabolites
B C
Metabolite 2
D E
F
Metabolite 3
Metabolite 4
I L
H K
Metabolite 5
Metabolite 6
Interpretation of Metabolome
25
Data Processing from FT-MS data acquisition of a time series experiment
to assessment of cellular conditions
10
(a) Metabolite quantities
for time series experiments
T8
T6 T7
T5
OD600
T4
(b) Data preprocessing and
constructing data matrix
T3
T2
T1
1
E. coli
0.1
0
200
400
600
800
Time point
Time (min)
(c) Classification of ions into
metabolite-derivative group
(d) Annotation of ions as
metabolites
 x11

 x21
 .....

 x s1
x12 ..... x1 j ..... x1k ..... x1M 

..... ..... x2 j ... x2 k ..... x2 M 
..... ..... ..... ... ..... ..... ..... 

xs 2 ..... ..... ... ..... ..... xsM 
Metabolites
M
(e) Assessment of cellular condition
by metabolite composition
Detected Theoretical
m/z
m/z
Molecular
formula
Exact mass Error
Candidate
Species
72.9878
73.9951
C2H2O3
74.0004
0.0053 Glyoxylic acid Escherichia coli
143.1080
144.1153
C8H16O2
144.1150
0.0003 Octanoic acid Escherichia coli
662.1037
663.1109
C21H27N7O14P2
663.1091
0.0018
NAD
Escherichia coli
664.1095
665.1168
C21H29N7O14P2
665.1248
0.0080
NADH
Escherichia coli
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
M/2
M+1
m/z
(b) Data matrix
metab.1
metab.200
x
x
.....
x
.....
x1k ..... x1M 
time 1  11 12
1j


time 2  x21 ..... ..... x2 j ... x2k ..... x2 M 
..... ..... ..... ..... ... ..... ..... .....
time

 xs1

 .....
 xt1

 .....
8  xN1

xs 2 ..... ..... ... ..... ..... xsM 

..... ..... ........ ..... ..... ..... 
xt 2 ..... xtj ... ..... ..... xtM 

..... ..... ........ ..... ..... ..... 
xN 2 ..... xNj ... xNk ..... xNM 
719.4869
747.5112
722.505
Software are provided by T. Nishioka (Kyoto Univ./Keio Univ.)
27
M-12
M-11
5
6
9
(c) Classification
M-8 of ions into metabolite4
derivative group (DPClus)
3
M-9
M-10
M-14
Correlation
M-5
network for individual ions.M-4
M-7
2-3
8
10
M-157
M-6
Intensity ratio between Monoisotope
(M)
M-13
and Isotope
(M+1)
2-2
 # of Carbons in molecular formula:
M-16
11
M-17
PG9
PG3
PG10
1-3
1-1
M-3
M-2
1-4,5
M-1
PG4
PG7
PG6
PG1
2-1
PG2
PG8
PG5
1-6
1-2
28
(d) Annotation of ions as metabolites using KNApSAcK DB
Detected
m/za
Theoretical
m/z
Molecular
formula
72.9878
73.9951
C2H2O3
74.0004
0.0053 Glyoxylic acid
Escherichia coli
143.1080
144.1153
C8H16O2
144.1150
0.0003 Octanoic acid
Escherichia coli
253.2137
254.2210
C16H30O2
254.2246
0.0036 omega-Cycloheptanenonanoic acid
Alicyclobacillus acidocaldarius
253.2185
254.2258
C16H30O2
254.2246
0.0012 omega-Cycloheptanenonanoic acid
Alicyclobacillus acidocaldarius
281.2444
282.2516
C18H34O2
282.2559
0.0042 Oleic acid
Escherichia coli
C18H34O2
282.2559
0.0042 cis-11-Octadecanoic acid
Lactobacillus plantarum
Exact mass
Error
Candidate
Species
C18H34O2
282.2559
0.0042 omega-Cycloheptylundecanoic acid
Alicyclobacillus acidocaldarius
297.2410
298.2482
C18H34O3
298.2508
0.0026 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
297.2467
298.2540
C18H34O3
298.2508
0.0032 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
297.2516
298.2589
C18H34O3
298.2508
0.0081 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
321.0506
322.0579
C10H15N2O8P
322.0566
0.0013 dTMP
Escherichia coli K12
346.0570
347.0643
C10H14N5O7P
347.0631
0.0012 AMP
Escherichia coli
C10H14N5O7P
347.0631
0.0012 3'-AMP
Escherichia coli
C10H14N5O7P
347.0631
0.0012 dGMP
Escherichia coli
401.0168
402.0241
C10H16N2O11P2
402.0229
0.0012 dTDP
Escherichia coli
402.9962
404.0035
C9H14N2O12P2
404.0022
0.0013 UDP
Escherichia coli
426.0237
427.0310
C10H15N5O10P2
427.0294
0.0016 Adenosine 3',5'-bisphosphate
Escherichia coli
C10H15N5O10P2
427.0294
0.0016 ADP
Escherichia coli
C10H15N5O10P2
427.0294
0.0016 dGDP
Escherichia coli
C20H19Cl2NO7
455.0539
0.0075 Antibiotic MI 178-34F18A2
Actinomadura spiralis MI178-34F18
C20H19Cl2NO7
455.0539
0.0075 Antibiotic MI 178-34F18C2
Actinomadura spiralis MI178-34F18
454.0391
455.0464
458.1112
459.1185
C15H22N7O8P
459.1267
0.0083 Phosmidosine B
Streptomyces sp. strain RK-16
495.1039
496.1112
C24H20N2O10
496.1118
0.0006 Kinamycin A
Streptomyces murayamaensis sp. nov.
C24H20N2O10
496.1118
0.0006 Kinamycin C
Streptomyces murayamaensis sp. nov.
505.9908
506.9981
C10H16N5O13P3
506.9957
0.0023 ATP,dGTP
Escherichia coli
547.0756
548.0829
C16H26N2O15P2
548.0808
0.0020 dTDP-L-rhamnose
Escherichia coli
565.0503
566.0576
C15H24N2O17P2
566.0550
0.0025 UDP-D-glucose
Escherichia coli
C15H24N2O17P2
566.0550
0.0025 UDP-D-galactose
Escherichia coli
C17H27N3O17P2
607.0816
0.0032 UDP-N-acetyl-D-mannosamine
Escherichia coli
C17H27N3O17P2
607.0816
0.0032 UDP-N-acetyl-D-glucosamine
Escherichia coli
606.0775
607.0848
ADP-L-glycero-beta-D-mannoheptopyranose
618.0897
619.0970
C17H27N5O16P2
619.0928
0.0042
662.1037
663.1109
C21H27N7O14P2
663.1091
0.0018 NAD
Escherichia coli
Escherichia coli
29
(e) Estimation of cell condition based on a function of the composition of metabolites.
1
0.1
0
T4
T3
T2
T1
T5
200
T8
T6 T7
400
600
PLS (Partial Least Square regression model)
-- extract important combinations of metabolites.
N (biol.condition) << M (metabolites)
800
Metabolites
Time (min)
measurement points
OD600
10
cell condition
Responses
K=1
Y
N=8
M=220
X
PLS
cell condition
N=8
Y(Cell density)= a1 x1 +…+ aj xj +….+ aM xM
xj, the quantity for jth metabolites
30
(e) Assessment of cellular condition by metabolite composition
Detection of stage-specific metabolites
(PLS model of OD600 to metabolite intensities)
y(OD600 Cell Density)= a1 x1 +…+ aj xj +….+ aM xM
xj , the quantity for jth
aj > 0, stationary phase-dominant metabolites
aj < 0, exponential phase-dominant metabolites
MS/MS analyses
0.1
dTDP-6-deoxy-L-mannose
Parasperone A
UDP-glucose, UDP-galactose
UDP-N-acetyl-D-glucosamine
UDP-N-acetyl-D-mannosamine
aj
Lenthionine
omega-Cycloheptylnonanoate
omega-Cycloheptylundecanoate, cis-11-Octadecanoic acid
UDP
Octanoic acid
dTMP, dGMP, 3'-AMP
NADH
PG2,4,6,8,10
80 metabolites
0.0
120 metabolites
Argyrin G
omega-Cycloheptyl-alpha-hydroxyundecanoate
ATP, dGTP
omega-Cycloheptyl-alpha-hydroxyundecanoate
dTDP
Glyoxylate
PG1,3,5,7,9
MS/MS analyses
-0.15
Exponential-phase dominant
ADP, Adenosine 3',5'-bisphosphate, dGDP
ADP-(D,L)-glycero-D-manno-heptose
Red: E.coli metabolites;Black: Other bacterial metabolites
NAD
Stationary-phase dominant
10 Phosphatidylglycerols detected by MS/MS spectra
O
O
unsaturated PGs
C15H31
O
O
X3
O
O
O
C15H31
O
O
X3
O
cyclopropanated PGs
Exponential phase
Cyclopropane
Formaiton of PGs
Stationary phase
(b) Relation of mass differences among PG1 to 10
marker molecules
PG1 ∆(CH )
PG3
(Cluster 1)PG5 ∆(CH )
28.0281
28.0315
30:1(14:0,16:1)
32:1(16:0,16:1)
34:1(16:0,18:1)
2 2
2 2
US
CFA 14.0170
CFA 14.0187
2.0138
PG7 ∆(CH )
PG9
28.0330
34:2(16:1,18:1)
36:2(18:1,18:1)
CFA 14.0110
2 2
PG6 ∆(CH )
PG2 ∆(CH )
PG4 CFA 14.0181
28.0298
28.0237
31:0(14:0,c17:0)
33:0(16:0,c17:0)
34:5(16:0,c19:0)
2 2
2 2
US
2.0051
PG8 ∆(CH ) PG10
28.0314
35:1(16:1,c19:1)
37:1(18:1,c19:0)
2 2
(Cluster 2)
Cyclopropane
CFA 14.0197
Formation of PGs occurs in the transition
from exponential to stationary phase.
Self organizing Maps
Time-series Data
Growth curve
10
j
…
1
…
T
2
0.1
1
0.01
Time
Expression profiles
Gene1
Gene2
...
Genei
...
GeneD
Stage
 x11

 x21
 ...

 xi1
 ...

 xD1
1
x12
...
x22
... x1 j
... x2 j
...
xi 2
...
...
...
xij
...
...
...
...
...
...
x D 2 ... x Dj
...
2
…. j
...
x1T 

x2T 
... 

xiT 
... 

x DT 
… T
When we measure time-series microarray, gene expression profile is represented by a matrix
SOM makes it possible to examine gene similarity and stage similarity simultaneously.
 x1 
 
 x2 
 ... 
 
 xi 
 ... 
 
 x D 
T, # of time-series microarray experiments
D, # of genes in a microarray
Time-series Data
Growth curve
10
j
…
1
0.1
…
T
2
1
0.01
Time
Expression profiles
 x11 x12

 x21 x22
...
 ...
...

Genei  xi1 xi 2
 ...
...
...

GeneD  xD1 x D 2

Stage 1
2 ….
Gene1
Gene2
... x1 j
... x2 j
...
...
...
...
xij
...
...
...
...
...
... x Dj
...
j
...
x1T 

x2T 
... 

xiT 
... 

x DT 
… T
…
…
Stage similarity
STATES
State-Transition
When we measure time-series microarray, gene expression profile is represented by a matrix
SOM makes it possible to examine gene similarity and stage similarity simultaneously.
 x1 
 
 x2 
 ...  Expression similarity
 
 xi 
 ... 
 
 x D 
T, # of time-series microarray experiments
D, # of genes in a microarray
Multivariate Analysis
SOM : expression similarity of
genes and stage similarity
simultaneously.
BL-SOM is available at
http://kanaya.aist-nara.ac.jp/SOM/
SOM was developed by Prof. Teuvo Kohonen in the early 1980s
Multi-dimensional data/input vectors are mapped onto a two
dimensional array of nodes
In original SOM, output depends on input order of the vectors.
To remove this problem Prof. Kanaya developed BL-SOM.
[1] Initial model vectors are determined based on PCA of the data.
[2] The learning process of BL-SOM makes the output independent
of the order of the input vectors.
SOM Algorithm
Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.
SOM Algorithm
Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.
SOM Algorithm
Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.
SOM Algorithm
in Fig. before
Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.
Self-organizing Mapping
(Summary)
X
[1] Detection method for transition points in
metabolite quantity based on batch-learni
(BL-SOM)
1
[2] Diversity of metabolites in species
 Species-metabolite relation Database
XT
X2
Gene i (xi1,xi2,..,xiT)
Gene1
Gene2
...
Genei
...
GeneD
 x11

 x21
 ...

 xi1
 ...

 xD1
x12
...
x22
... x1 j
... x2 j
...
xi 2
...
...
...
xij
...
...
...
...
...
...
x D 2 ... x Dj
...
...
x1T 

x2T 
... 

xiT 
... 

x DT 
 x1 
 
 x2 
 ... 
 
 xi 
 ... 
 
 x D 
T, different time-series microarray experiments
Self-organizing Mapping (Summary)
Arrangement of lattice points in
multi-dimensional expression
space
X1
Lattice points are optimized for reflecting data
distribution
Gene Classification
Genes are classified into the nearest lattice points
XT
X2
Gene i (xi1,xi2,..,xiT)
Self-organizing Mapping (Summary)
Arrangement of lattice points in
multi-dimensional expression
space
X1
Lattice points are optimized for reflecting data
distribution
Gene Classification
Genes with similar expression profiles are clusterized to
identical or near lattice points
X1 (Time 1)
Feature Mapping
X2 (Time 2)
In the i-th condition,
lattice points containing only highly
(low) expressed genes are colored by
red (blue).
XT
X2
(ex.)
Xk> Th.(k)
Xk< -Th.(k)
X3 (Time 3)
k=1,2,…,T
…..
…..
…..
XT (Time T)
Visually comparing among
each stage of time-series data
Non-linear projection of multi-dimensional expression profiles of genes.
Original dimension is conserved in individual lattice points.
Several types of information is stored in SOM
Estimation of transition points; Bacillus subtilis (LB medium)
(Data: Kazuo Kobayashi, Naotake Ogasawara (NAIST))
Stage 1
2
3
4
5
6
7
High prob.
10
Cell Density
(OD600 )
0
6
5
1
7
8
4
3
log(Prob. Density)
2
0.1
-1000
1
0.01
LB
0.001
-2000
0
200
400
600
800
1000
Low prob.
(min)
SOM for time-series expression profile
State transition point is observed between stages 3 and 4
8
Integerated analysis of gene expression profile and metabolite quantity data of Arabidopsis thaliana
(sulfur def./cont.; Data are provided by K.Saito, M. Hirai group (PSC) )
ppm(error rate)
Nakamura et al (2004)
State transition
Feature Maps
Leaf
Leaf
Gene
Metabolites
(m/z)
Root
Lattice points with
highly difference
between 12 and 24 h.
Blue: Decreased
Red: increased
Accurate molecular weights
 Candidate metabolites corresponding to accurate molecular weights
3.
Species-metabolite relation Database
Root
Download sites of BL-SOM
Riken： http://prime.psc.riken.jp/
NAIST: http://kanaya.naist.jp/SOM/
Application of BL-SOM to “-omics”
Genome
Kanaya et al., Gene, 276, 89-99 (2001)
Abe et al., Genome Res., 13, 693-702, (2003)
Abe et al., J.Earth Simulator, 6, 17-23, (2003)
Abe et al., DNA Res., 12, 281-290. (2005)
Transcriptome
Haesgawa et al., Plant Methods, 2:5:1-18 (2006)
Metabolome
Kim et al., J. Exp.Botany, 58, 415-424, (2007)
Fukusaki et al., J.Biosci.Bioeng., 100, 347-354, (2005)
Transcriptome and Metabolome
Hirai, M. Y., M. Klein, et al. J.Biol. Chem., 280, 25590-5 (2005)
Hirai, M. Y., M. Yano, et al. Proc Natl Acad Sci U S A 101, 10205-10 (2004)
Morioka, R, et al., BMC Bioinformatics, 8, 343, (2007)
Yano et al., J.Comput. Aided Chem.,7,125-136 (2007)
Summary of Bioinformatics Tool developed in our laboratory
http://kanaya.naist.jp/~skanaya/Web/JTop.html
All softwares and DB are freely accessable via Web.
Metabolomics
-- MS data processing
Transcriptome and Metabolomics Profiling
-- estimation of transition points
Species-metabolite DB
Network analysis: PPI
Transcriptomics
-- Statistics, Profiling, …
Introduction to self organizing mapping software
&
Introduction to software package Expander
http://acgt.cs.tau.ac.il/expander/