This document discusses computational tools for analyzing biological networks and molecules. It begins by describing how networks can be inferred from molecular data using the Cytoscape platform and its apps. It then discusses how networks can help identify proteins of interest by describing a case study where network analysis identified additional proteins that provided an "explanation" for differences observed in proteomic data. The document concludes by discussing how computational analysis can help address questions that go beyond simply identifying the best network, such as which parts of a network are well-supported or could be modified to better fit the data.

1. Computa(onal
tools
for
going
from
molecules
to
interac(ons
...and
back
Benno
Schwikowski
Systems
Biology
Lab
Ins(tut
Pasteur,
Paris

2. Phenotype
Adapted
from
E.
Zerhouni’s
talkKohn, 1999

3. 20,000 200,000,000
Molecules Interac(ons

4. Nature
News,
18
July
2012 Reactome,
18
July
2013

5. From
molecules
to
networks
Network
inference
in
Cytoscape
3
From
networks
to
molecules
How
networks
can
help
to
iden(fy
proteins

6. Cytoscape
Open-­‐source
plaLorm
for
biological
network
data
integra(on,
analysis,
and
visualiza(on
– Free
&
Open-­‐source
(LPGL)
– Developed
and
maintained
by
universi(es,
companies,
and
research
ins(tu(ons
– Expandable
by
Apps/Plugins
6

7. Show
the
results
7
VizMapper
Layouts
Cytoscape
Apps
Visualiza3on
Computa3onal
Analysis
Human
analysis
Filtering
Selec(on
Data
import
Data
export
Cytoscape
Workflow

8. Annotated
Network
Core
Concepts
-­‐
Integra(on
• Networks
&
Data
Tables
(A[ributes)
8

9. VizMapper
Core
Concepts
-­‐
Visual
mapping
9
Use
specific
line
types
to
indicate
different
types
of
interac(ons
Browse
extremely
dense
networks
by
controlling
for
the
opacity
of
nodes Expression
data
mapping
Set
node
sizes
based
on
the
degree
of
connec(vity
of
the
nodes
Encode
specific
physical
en((es
as
different
node
shapes
Data
Table

10. Core
Concepts
-­‐
Analysis
Apps/Plugins:
Expanding
Cytoscape
Func(onality
10

11. Berlin,
July
18,
2013
Import
Networks
11
• Network
Data
Formats
– SIF
– GML
– XGMML
– GraphML
– BioPAX
– PSI-­‐MI
– SBML
– KGML(KEGG)
– Excel
– Delimited
Text
Table
– CSV
– Tab
• Network Databases
– Protein - Protein
– STRING - IntAct
– Genetic
– BioGRID
– Protein - Compound
– ChEMBL
– Human-Curated
Pathways
– KEGG, Reactome,
PathwayCommons

12. Berlin,
July
18,
2013
Import
Data
Table
(A[ributes)
• Data
Table:
Any
data
that
describes
or
provides
details
about
nodes,
edges,
and
networks
• Anything
saved
as
a
table
can
be
loaded
into
Cytoscape
– Excel
– Tab
Delimited
Document
– CSV
• As
long
as
proper
mapping
key
is
available,
Cytoscape
can
map
them
to
your
networks
12
BRCA1
GO Terms:
DNA Repair
Cell Cycle
DNA Binding
NCBI Gene ID 672
On Chromosome 16 Ensemble ID
ENSG00000012048
Public
Data
Sources

13. Berlin,
July
18,
2013
What’s
new
in
3.0
13

14. • 2.x
done
without
explicit
design
guidelines
or
standards
• No
well-­‐defined
API
• Hard
to
maintain
and
improve
(plugins
breaking)
• Plugins
could
not
share
func^onality
Berlin,
July
18,
2013
Cytoscape
3
–
Reasons
for
the
rewrite
14

15. Berlin,
July
18,
2013
Cytoscape
3.0
–
A
complete
rewrite
• New
modular
architecture
based
on
OSGi
• Compa^bility
with
3.0
guarantees
compa^bility
with
3.x
• Clear
and
simplified
API
(implementa^on
separate)
• RootNetwork/SubNetwork
design
• Acributes
are
replaced
by
Tables
(‘first-­‐class
ci^zens’)
– CyRow
and
CyColumn
interfaces
• Apps
can
talk
to
each
other
now,
much
less
likely
to
break
• All
plugins
need
to
be
converted
to
Apps
15

16. • 140+ plugins for version 2.x series
• 16 apps for 3.x series
Berlin,
July
18,
2013
Status of apps/plugins
16
3.0 Apps
jActiveModules
MCODE
AgilentLiterature Search
VennDiagramGenerator
ClusterONE
Centiscape
GeneMANIA
Integrated in 3.0 Core
EnhancedSearch
BiomartClient
NetworkAnalyzer
Plugins
being
ported
ClusterMaker
Genoscape
MiMiplugin
...

17. Berlin,
July
18,
2013
What’s
new
in
3.0
• hcp://apps.cytoscape.org
17

18. Cytoscape 3.x
Cyni Toolbox
GUI
Cyni API
- Cyni Interfaces
- Cyni Data Structure
- Utility Methods
Data
Imputation
Network
Inference
Data
Discretization
Metrics
Cyni Apps
User 2: Method Developer
New Network
Inference Method
User 1: Biologists

19. Load Data
Berlin,
July
18,
2013
Cyni network inference
19
Estimate Data Discretize Data Infer Network

20. Berlin,
July
18,
2013
Cyni
Network
inference
toolbox
• Cyni
provides
– A
few
built-­‐in
algorithms
– Data
imputa^on
and
discre^za^on
techniques
– Several
known
metrics
(correla^on,
bayesian,...)
– Documented
API
– Tutorials
and
sample
code
• First
3.0
app
that
exports
func^onality
• Addi^onal
implementa^ons
underway
(ARACNe)
20

21. From
molecules
to
networks
Network
inference
in
Cytoscape
3
From
networks
to
molecules
How
networks
can
help
to
iden(fy
proteins

22. Motivation
• Study of 24 smooth muscle cells over many
years
• Proteomic analysis of many samples revealed
systematic differences between two groups
• Close analysis revealed that the causative
factor is the use of bovine DNAse I in the
protein extraction protocol
22

23. Affected SMC protein extracts
3 104 5 6 7 8 9
43
34
26
55
95
130
17
11
Unaffected SMC protein extracts
43
34
26
55
95
130
17
11
3 104 5 6 7 8 9
DIGE
Without
DNAse I
treatment
DIGE
With
DNAse I
treatment
Acosta-Martin, Gwinner, Pinet, Schwikowski, unpublished

24. First bioinformatic analysis
• 11 unaffected and 13 affected SMC protein
extracts (as identified by absence of 3 large
spots)
• 569 out of 853 spots differentially expressed,
408 with FC>2, 135 significant (62 down, 73
up)
• Identification of 41 proteins from 102 spots
• GO analysis: >50% in apoptosis, cell motion,
actin cytoskeleton reorganization
24

25. The Steiner tree approach
• “Explanation”=
connected
network
• Parsimony
principle: Use
the minimum
number of
additional
proteins
25

26. Steiner PPI analysis
• Started with 41 original proteins + DNAse I –
ACAP1 (unconnected)
• Use BIND and IntAct databases:
–51,975 interactions among 21,022 proteins
• Weight edges with inverse functional similarity
score (between 0 and 10)
• Use Steiner heuristic implemented in the
GOBLIN tool (Univ. Augsburg)
26
Schlicker (2007), Nucleic Acids Research
Mehlhorn (1988) Information Processing Letters

27. Sanity check: Is the resulting network
better than chance?
27
Network length Number of Steiner nodes

28. Resulting Steiner network
28
Gwinner et al. (2013), Proteomics

29. Resulting list of Steiner nodes
29
• Focus on Steiner nodes with meaningful
connections to input proteins:
Sort by score sum over all interactions to
input proteins

30. 55 kDa 43 kDa
ArbitraryUnits/1000
ArbitraryUnits/10
Experimental validation
Gwinner et al., Proteomics (2013)

31. From
molecules
to
networks
Network
inference
in
Cytoscape
3
From
networks
to
molecules
How
networks
can
help
to
iden(fy
proteins

32. Galagan
et
al.,
Nature
499
(11
July
2013)

33. 33
Large-scale
measurement
Biology
Computation
Manipulate
Measure
Mine
Model
Ideker/Lauffenburger
2006

34. Berlin,
July
18,
2013
Questions beyond ‘the best network’
• Which parts of a given network are consistent with
the data?
• Which parts of the network are we sure of, given the
data?
• Which interactions could be added (removed) to
make the data compatible with the model?
• Which experiment could be done to better
distinguish different possible models?
34

35. Postdocs
Ph.D.
students
Senior
So@ware
Engineer
Xiaoyi
Chen
Oriol
GuitartFreddy
Cliquet
Frederik
Gwinner
Robin
Friedman
Master
students
Iryna
Nikolayeva
Systems
Biology
Lab
Leif
Blaese

36. Steiner
approach
Adelina
Acosta-­‐Mar(n,
Florence
Pinet
(Inst.
Pasteur
Lille)
Cytoscape/Cyni
Part
of
Gary
Bader
&
Co.
(U.
Toronto)
Alexander
Pico
&
Co
(Gladstone
SFO)
Trey
Ideker
&
Co.
(UC
San
Diego)
Chris
Sander
&
Co.
(MSKCC
NYC)
Piet
Molenaar
Agilent
Leroy
Hood
&
Co.
(ISB
Sea[le)
Collaborators
36

37. Berlin,
July
18,
2013
Cytoscape Retreat 2013
Pasteur Institute, Paris
Oct 9: Symposium on Network Biology
Oct 10: Cytoscape User and Developer Tutorials
http://nrnb.org/cyretreat/
37