Multiple-Scale Visualization and
Modeling of Biological
Networks/Pathways
Zhenjun Hu
Bioinformatics Program,
Boston University, Boston, MA02215
http://visant.bu.edu
Outlines
• Multiscale visualization & modeling using metagraph
– Distinguished features of biological networks
– Handling large-scale networks
– Advanced graphs & multiscale visualization & modeling
• Existing compound graph
• Metagraph: an extension of compound graph, or an alternative
of hypergraph that can be used for pictorial representation.
– Metagraph for pathway visualization
– Hierarchical visualization, integration & modeling
• Potential applications of metagraph for social
networks
2
Why networks
Circuit diagrams for biological networks ?
The enthusiasm of the biological networks probably comes from the
successful stories of the circuit diagrams in electronics.
An early stored-program computer (left), built around 1950, used
vacuum tubes in logic circuits, whereas modern computers use
transistors and silicon wafers (right), but both are based on the
same principles.
3
Hartwell LH, Hopfield JJ, Leibler S et al. From molecular to modular cell biology, Nature 1999;402:C47-52
Why graphs
Circuit diagrams for biological networks ?
Tools for mining and visualizing cell systems has moved beyond static
pictures of networks and links, most of them are based on the types of
graphs listed below:
Simple graph: contains no selfloops or multiple edges between
pairs of nodes.
Multigraph: Allows multiple
edges between pairs of nodes.
Compound graph: Integrates both
adjacency relations (correlations
between pairs of nodes) and inclusion
relations among nodes (that is, simple
nodes within a larger ‘compound’ node
such as the ellipse around the simple
nodes, A and B). Compound nodes
cannot intersect one another
When knowledge is integrated:
simple graph multigraph/hybrid graph compound graph
4
What features a biological network
However, there are fundamental differences between biological
networks and logic circuits:
Scale: There are thousands of biomolecules, such as genes,
RNAs, and proteins, each may have different states.
Abstract: Each node represents thousands of copies of the same
biomolecule.
Dynamic: The biological networks are changing dynamically,
components may appear or disappear under certain condition.
(Modular): Biological networks may have a modular nature, and
may organized in a hierarchical structure.
5
Handling large-scale networks
There are two key aspects need to be addressed when
handling large-scale networks:
• System performance.
–
–
–
–
–
Memory handling
Right data structure
Avoid nice drawing
Compact size
Batch mode
• Network readability.
– Better zooming/layout?
– Not much we can do?
6
Handling large-scale networks
Batch mode. This mode reads instructions from a command file, and process the requests
without any visual interface and user interactions, which enables VisANT to run in the
background ( http://visant.bu.edu/vmanual/cmd.htm ).
•
Command to run (assume the command file is located under res directory and the name is
“batch_cmd.txt”):
java -Xmx512M -Djava.awt.headless=true -jar VisAnt.jar -b res/batch_cmd.txt
•
Sample input/output:
7
Handling large-scale networks
A functional linkage network with
15,447 nodes and 1,722,708
edges and laid out using elegant->spring-embedded relaxing, as
shown at right.
The data of the network is
downloaded from
http://www.functionalnet.org/mous
enet/ and directly loaded into
VisANT on a duocore computer
with 2G memory and win XP. Be
aware that we specified the
maximum memory size that are
available on the test machine in
the run.cmd: 1424M, which may
not be required by this network
and you can therefore reduce it in
case necessary. In addition,
VisANT can now directly read the
zip file therefore the downloaded
data is zipped. It takes 5+ hours
for the test case to finish
8
Handling large-scale networks
81,287
9
Handling large-scale networks
• So far we have discussed the solutions to improve
system performance using the methods of the
software engineering. But there seems no good
solution to improve the network readability.
• We will discuss how to use the advanced graph to
improve the network readability and system
performance by integrating more biological
information
An interaction network with 5489 nodes and 29,983 edges
(Y2H:blue and Phylo: green)
10
Advanced graphs & multiscale visualization
& modeling
How geographical map zooms
Countries
…
TX
MA
States
Cities
Blocks
11
Advanced graphs & multiscale
visualization & modeling
Semantic zooming vs. geometric zooming
• Geometric (standard) zooming: The view depends on the
physical properties of what is being viewed, objects change only
their size.
• Semantic zooming: Different representations for different
spatial scales. The objects being viewed can additionally
change shape, details (not merely size of existing details) or,
indeed, their very presence in the display, with objects
appearing/disappearing according to the context of the map at
hand.
• Biological network is much more complicated than
geological maps
12
Advanced graphs & multiscale
visualization & modeling
Behind the scenes: compound graph= inclusive tree + adjacency graph
A
B
H
C
G
M
D
K
A
H
B
G
E
C
M
inclusive tree
F
D
A
H
B
K
E
F
G
C
M
D
K
E
F
adjacency graph
13
Sugiyama, K. & Misue, K. Visualization of structure information: Automatic drawing of compound digraphs. IEEE Trans. Systems, Man, and
Cybernetics 21, 876-892 (1991).
Advanced graphs & multiscale
visualization & modeling
Compound graph continued.
A
H
B
G
C
M
D
K
E
F
A
H
B
G
Two restrictions
1. No intersection between groups
2. An rooted inclusive tree
C
M
D
K
E
F
14
Sugiyama, K. & Misue, K. Visualization of structure information: Automatic drawing of compound digraphs. IEEE Trans. Systems, Man, and
Cybernetics 21, 876-892 (1991).
Advanced graphs & multiscale
visualization & modeling
•
•
Except the leaf node, each node in the inclusive tree can be thought as a group containing
nodes of next detail level. From the point view of biological networks, such group can be a
functional module, a protein complex etc.
And a biological network seems have a modular structure:
15
Advanced graphs & multiscale
visualization & modeling
And life complexity seems hierarchical
16
Oltvai, Z.N. & Barabasi, A.L. Systems biology. Life’s complexity pyramid. Science 298,763–764 (2002).
Advanced graphs & multiscale
visualization & modeling
And metabolic network seems to have a hierarchical organization
17
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N. & Barabasi, A.L. Hierarchical organization of modularity in metabolic networks. Science 297,
1551–1555 (2002).
Advanced graphs & multiscale
visualization & modeling
It seems that we can use compound graph to turn a “hair ball” of interaction network
into a much readable network of functional modules:
18
Tucker, C.L., J.F. Gera, and P. Uetz, Towards an understanding of complex protein networks. Trends Cell Biol, 2001. 11(3): p. 102-6
Advanced graphs & multiscale
visualization & modeling
• However, biological modules usually
overlaps, because biomolecules usually play
multiple roles. But compound graph does not
support overlapping between groups
• But why the complicated circuit diagram in
electronics does not have overlapping
problem?  A biological network is an abstract network
19
Advanced graphs & multiscale
visualization & modeling
• Metagraph definition
Gm  {V , E}
V  {Vs ,Vm }
E  {Es , Em }
20
Hu Z, Mellor J, Wu J et al. Towards zoomable multidimensional maps of the cell, Nat Biotechnol 2007;25:547-554
Advanced graphs & multiscale
visualization & modeling
• Metanode definition
Expanded
vm  Vm
v V
A
Collapsed
B
C
v  vi i  0
21
Hu Z, Mellor J, Wu J et al. Towards zoomable multidimensional maps of the cell, Nat Biotechnol 2007;25:547-554
Advanced graphs & multiscale
visualization & modeling
• Metaedge definition: transient
em  Em  evm ,v
evm ,v  g (vm , v)
22
Hu Z, Mellor J, Wu J et al. Towards zoomable multidimensional maps of the cell, Nat Biotechnol 2007;25:547-554
Advanced graphs & multiscale
visualization & modeling
• Metagraph illustration
Illustration of the dynamics of meta graph. (I) An eight gene
network grouped into three metanodes (G1, G2, G3), each
containing a set of genes that subserve some common function.
E
I
The idea that a node, such as C, is known to participate in more
than one function at a given level, is represented by displaying it G1
A
in more than one metanode. Three meta-nodes are in
expanded state and their internal network structure is visible. (II)
Meta-node G2 is collapsed and three meta-edges H_G2 (=H_B),
G3 G
E_G2 (=E_B) and C_G2 are created based on the original
network connectivity. Meta-edge C_G2 is a special edge
because it represents the shared components and rendered
E
I
using a dashed line. (III) Both G1 and G2 are collapsed, three
meta-edges are created, with G1_G2=E_G2 + H_G2,
A
G1
G1_G3=A_G and G3_G3=C_G2. It has also been shown here
that meta-node can be embedded, with G1 and G3 embedded in
G3 G
a new meta-node G4. (IV) meta-node G4 collapsed, with a new
meta-edge G4_G2=G1_G2+G3_G2. The procedures between I,
II, III and IV are reversible. This might be best explained in
terms of GO levels. For example G1, G2 and G3 might be GO
level 10 (pathway level) whereas G4 is GO level 9 etc.
B
G2
H
C
G2
G4
F
C
I IV
II III
G2
H
G1
G2
G4
F
C
G3
23
Advanced graphs & multiscale visualization
& modeling
An example to use metagraph to improve the readability and performance
24
Total: 5,321 nodes and 33,992 edges
Advanced graphs & multiscale visualization & modeling
An example to use metagraph to improve the readability and performance (continued)
25
Total: 5,321 nodes and 33,992 edges
Advanced graphs & multiscale visualization
& modeling
An example to use metagraph to improve the readability and performance (continued)
26
Total: 5,321 nodes and 33,992 edges
Metagraph for pathway visualization
• Metagraph application in pathway visualization
C
KEGG Pathway Diagram
(part of G1 phase of cell cycle)
Complex Hierarchy
A
B
E
27
Metagraph for pathway visualization
• Metagraph application in pathway visualization (continued)
I
II
Improved readability and performance with multi-scale I
information integrated in pathway visualization using
metagraph. Blue boxes represent the KEGG pathways;
blue boxes with dark border are contracted metanodes
representing a group of proteins; orange boxes with
light border representing the protein complex, filled
circles represent protein and open circles represent
compounds. (I) Five signaling pathways of Homo
sapiens visualized using metagraph, dashed lines
indicate that there are shared nodes. (II) Same number
of pathways visualized as an interaction network. The
size of the node is reduced to improve the readability.
28
Hu Z, Snitkin ES, DeLisi C. VisANT: an integrative framework for networks in systems biology, Brief Bioinform 2008;9:317-325
Metagraph for pathway visualization
• Condition dependency
29
Hu Z, Snitkin ES, DeLisi C. VisANT: an integrative framework for networks in systems biology, Brief Bioinform 2008;9:317-325
Hierarchical visualization, integration
& modeling
• Metagraph application: visualization of the network hierarchy
Level 4
Level 3
Module of level 3
Protein of level 4
Level 1
Level 2
Level 1: 1 module
Level 2: 8 modules
Level 3: 161 modules
Level 4: 810 proteins. Only part of proteins are shown
in the figure due to space limit.
30
Hu Z, Mellor J, Wu J et al. Towards zoomable multidimensional maps of the cell, Nat Biotechnol 2007;25:547-554
Hierarchical visualization, integration
& modeling
•
Metagraph application: integrating interaction network with GO
hierarchical modules
A
sequence-specific
DNA binding
0(+34) genes
B
centromeric rDNA
AT DNA telomeric DNA DNA replication
DNA binding Binding Binding Binding
origin binding
6 genes
6 genes 3 genes 9 genes
10 genes
C
D
31
Hu Z, Mellor J, Wu J et al. Towards zoomable multidimensional maps of the cell, Nat Biotechnol 2007;25:547-554
•
Hierarchical visualization, integration
&
modeling
Metagraph application: network of protein complexes
32
Gavin, A.C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).
•
Hierarchical visualization, integration
& modeling
Metagraph application: network of protein complexes integrated with
Y2H interactions
33
Hierarchical visualization, integration
& modeling
• bottom-up modeling: cancer network
34
Goh KI, Cusick ME, Valle D et al. The human disease network, Proc Natl Acad Sci U S A 2007;104:8685-8690.
Hierarchical visualization, integration
& modeling
• top-down modeling: disease networkcancer gene network
35
Goh KI, Cusick ME, Valle D et al. The human disease network, Proc Natl Acad Sci U S A 2007;104:8685-8690.
Quick summary
• Metagraph improves the network readability and system
performance with integrated context information.
• Metagraph helps to represent the complication of the biological
network, such as condition-dependency, combinatory control
etc.
• Metagraph extends the system’s capability to integrate
multiscale knowledge, making it much more practical to
model/simulate the complexity of biological system: from cell to
functional module, network motif, protein…
36
Metagraph: potential application in
social network
• Science of Science and Innovation Policy (SciSIP)
37
Metagraph: potential application in
social network
• What can be expected from SciSIP?
1. Predict potential research innovation
2. Predict potential new cross-discipline research fields
3. Predict potential collaboration between different research scientists
4. and more ……
38
Metagraph: potential application in
social network
• Let’s model each paper (blue) as a metanode with authors (red)
as its components and then we get a network of publications:
A collaboration network
between different research fields
39
Metagraph: potential application in
social network
• Let’s turn the publication network into co-author network:
More importantly, an author can also be
modeled as a metanode with educations,
hobbies etc. as the subcomponents, which
will enable us to draw the correlations from
heterogeneous data
40
Acknowledge
VisANT Community
Team of Development:
Zhenjun Hu, Boston Univ.
Evan Snitkin, Boston Univ.
Yan Wang, Boston Univ.
Bolan Linghu, Boston Univ.
Jui-Hung Hung, Boston Univ.
Collaborators:
IBM Watson Research Laboratory
KEGG Database
Stuart Lab
Center of Cancer System Biology
Joint Developers:
Takuji Yamada, Kyoto Univ.
Shuichi Kawashima, University of Tokyo
David M. Ng, UCSC
Chunnuan Chen, UCSC
Changyu Fan, CCSB, Harvard Medical
School
Veterans:
Joe Mellor, Harvard Medical School
Jie Wu, Boston Univ.
Advisory Board:
Aravind Iyer, Computational Biology
Branch, NCBI, NLM, NIH
Bart Weimer, Director, Center for
Integrated BioSystems, Utah State
University
Chris Sander, Sloan Kettering
Memorial Cancer Center
Daniel Segrè, Bioinformatics
Program, Boston University
Frederick Roth, Department of
Biological Chemistry and Molecular
Pharmacology, Harvard Medical
School
Joseph Lehár, Combinatorix, Inc
Josh Stuart, Biomolecular
Engineering, UCSC
Charles DeLisi
Part of the support funding come from NIH & Pfizer
41
Have fun
with your own networks!
42