BIOINFORMATICS

Systems

biology

REVIEW

Vol

no.

pages

doi

Tools

for

visually

exploring

biological

networks

Matthew

Suderman

and

Michael

Hallett

McGill

Centre

for

Bioinformatics

University

Street

Montreal

Canada

Received

on

February

revised

on

June

accepted

on

August

Advance

Access

publication

August

Associate

Editor

Jonathan

Wren

ABSTRACT

Many

tools

exist

for

visually

exploring

biological

networks

including

well-known

examples

such

as

Cytoscape

VisANT

Pathway

Studio

and

Patika

These

systems

play

a

key

role

in

the

development

of

integrative

biology

systems

biology

and

integrative

bioinformatics

The

trend

in

the

development

of

these

tools

is

to

go

beyond

static

representations

of

cellular

state

towards

a

more

dynamic

model

of

cellular

processes

through

the

incorporation

of

gene

expression

data

subcellular

localization

information

and

time-dependent

behavior

We

provide

a

comprehensive

review

of

the

relative

advantages

and

disadvantages

of

existing

systems

with

two

goals

in

mind

to

aid

researchers

in

efficiently

identifying

the

appropriate

existing

tools

for

data

visualization

to

describe

the

necessary

and

realistic

goals

for

the

next

generation

of

visualization

tools

In

view

of

the

first

goal

we

provide

in

the

Supplementary

Material

a

systematic

comparison

of

more

than

existing

tools

in

terms

of

over

different

features

Contact

msuder@mcb.mcgill.ca

Supplementary

information

Supplementary

data

are

available

at

Bioinformatics

online

INTRODUCTION

Networks

are

used

ubiquitously

throughout

biology

to

represent

the

relationships

between

genes

and

gene

products

Perhaps

due

to

their

simple

discrete

nature

and

to

their

amenability

to

visual

representation

biological

networks

allow

for

the

most

salient

properties

of

complex

systems

to

be

highlighted

in

a

succinct

and

powerful

manner

Visualization

of

biological

networks

range

from

small-scale

descriptions

of

specific

metabolic

regulatory

or

signalling

pathways

appearing

in

countless

journal

articles

seminars

and

courses

to

classic

efforts

such

as

the

Roche

Applied

Science

Biochemical

Pathways

-LRB-

Michal

-RRB-

and

the

Biochemical

Pathway

Atlas

-LRB-

Michal

-RRB-

that

attempt

to

provide

broad

maps

of

cellular

organization

Whereas

these

latter

efforts

required

extensive

manual

curation

to

iteratively

refine

the

maps

as

new

information

became

available

the

advent

of

high-throughput

genetic-based

assays

that

permit

-LRB-

near

-RRB-

cellwide

exploration

of

relationships

between

genes

and

gene

products

has

motivated

the

development

of

software

systems

capable

of

constructing

visualizations

of

these

datasets

on

the

fly

Assays

such

as

genetic-based

protein

protein

interaction

To

whom

correspondence

should

be

addressed

screens

-LSB-

e.g.

-LRB-

Fields

and

kyu

Song

Rigaut

et

al.

Selbach

and

Mann

-RRB-

-RSB-

RNAi-based

genetic

interaction

screens

-LRB-

Echeverri

and

Perrimon

Fire

et

al.

-RRB-

gene

expression

-LRB-

Lockhart

et

al.

-RRB-

and

ChIP-Chip

arrays

-LRB-

Blat

and

Kleckner

Ren

et

al.

-RRB-

are

readily

visualized

when

nodes

represent

genes

gene

products

or

small

molecules

and

links

-LRB-

edges

-RRB-

between

pairs

of

nodes

can

model

metabolic

events

protein

protein/protein

nucleotide

interactions

regulatory

relationships

or

signaling

pathways

Such

simple

networks

that

capture

a

static

snapshot

of

cellular

state

have

been

shown

capable

of

yielding

insight

into

the

underlying

biology

Network

analysis

e.g.

has

shown

that

many

types

of

biological

networks

across

a

broad

spectrum

of

organisms

share

important

graph-theoretic

properties

These

properties

suggest

testable

biological

hypotheses

that

may

in

turn

reveal

the

fundamental

design

principles

of

biological

systems

For

example

there

is

evidence

that

many

types

of

biological

networks

are

scale-free

-LRB-

Barabasi

and

Oltvai

-RRB-

containing

a

few

highly

connected

nodes

-LRB-

hubs

-RRB-

and

a

majority

of

nodes

linked

to

only

a

few

neighbors

-LRB-

Barabasi

and

Albert

-RRB-

The

fundamental

hypothesis

here

is

that

scale-free

networks

are

robust

to

random

node

removals

since

a

majority

of

the

nodes

are

not

essential

to

cellular

function

and

simultaneously

fragile

because

targeted

removal

of

a

few

hubs

will

disrupt

cellular

function

This

robust

yet

fragile

network

property

is

thought

by

some

to

be

responsible

for

the

ability

of

biological

systems

to

survive

a

wide

variety

of

attacks

while

being

susceptible

to

a

few

specific

targeted

attacks

-LRB-

Albert

et

al.

-RRB-

Network

models

have

facilitated

a

shift

from

the

study

of

evolutionary

conservation

between

individual

gene

and

gene

products

towards

the

study

of

conservation

at

the

level

of

pathways

and

complexes

In

particular

Kelley

et

al.

-LRB-

-RRB-

show

that

the

protein

protein

interaction

networks

of

two

distantly

related

species

Saccharomyces

cerevisiae

and

Helicobacter

pylori

contain

many

of

the

same

or

very

similar

pathways

In

many

of

these

conserved

pathways

the

S.cerevisiae

network

appears

to

be

more

specialized

Resultant

tools

such

as

PathBLAST

-LRB-

Kelley

et

al.

-RRB-

are

now

commonly

used

to

automate

the

discovery

of

similarities

-LRB-

alignments

-RRB-

within

and

between

protein

protein

interaction

networks

These

and

other

successes

indicate

that

network

modeling

is

a

promising

approach

however

visualization

in

this

domain

remains

a

difficult

challenge

for

several

reasons

First

the

increasing

number

of

interactions

available

for

even

small

genomes/proteomes

presents

significant

visualization

challenges

The

Author

Published

by

Oxford

University

Press

All

rights

reserved

For

Permissions

please

email

journals.permissions@oxfordjournals.org

M.Suderman

and

M.Hallett

ER

lumen

Vacuole

lumen

Nucleus

ER

membrane

Golgi

membrane

Golgi

periphery

ER

periphery

Cytosol

PM

periphery

PM

PM

periphery

-LRB-

outside

cell

-RRB-

Vacuole

periphery

Mitochondrial

lumen

Fig.

Protein

protein

interaction

map

showing

predicted

subcellularlocalizations

of

all

proteins

in

the

secretory

pathway

-LRB-

Scott

et

al.

-RRB-

The

drawing

was

created

using

the

Cytoscape

plugin

Cerebral

-LRB-

Barsky

et

al.

-RRB-

Subcellular

compartments

are

ordered

so

that

compartments

with

the

most

inter-compartmental

links

are

next

to

one

another

For

practical

reasons

including

simplicity

and

computational

tractability

most

network

models

can

not

handle

such

n-ary

relations

Consequently

general

methods

of

extending

models

such

as

by

using

n-ary

relations

are

not

practical

Extensions

based

on

domain-specific

knowledge

is

required

Finally

there

is

a

general

need

for

the

biological

network

tools

to

incorporate

more

dynamic

information

within

the

network

A

first

step

in

this

direction

is

the

inclusion

of

gene

expression

data

within

several

existing

visualization

tools

The

mapping

of

gene

expression

-LRB-

represented

typically

by

color

-RRB-

onto

network

nodes

can

be

viewed

as

our

first

glimpse

of

large-scale

systems-wide

dynamics

in

biological

systems

However

current

functionality

is

not

capable

of

addressing

the

computational

and

statistical

challenges

that

will

arise

as

researchers

begin

to

construct

largescale

probabilistic

and

dynamic

models

of

complicated

distributed

biological

processes

nor

is

it

capable

of

addressing

how

dynamic

models

may

be

visually

represented

The

purpose

of

this

review

is

First

it

is

designed

to

provide

readers

with

an

overview

of

existing

software

systems

for

biological

network

visualization

-LRB-

Section

-RRB-

The

existence

of

at

least

such

systems

precludes

an

in-depth

review

of

each

instead

we

have

sought

to

single

out

systems

that

contain

unique

functionality

that

have

broad

applicability

in

many

different

studies

Furthermore

we

discuss

some

of

the

most

important

features

currently

available

in

network

visualization

packages

and

summarize

the

tools

that

support

them

-LRB-

Section

-RRB-

Second

we

discuss

the

future

of

network

visualization

and

how

current

tools

can

be

improved

to

satisfy

future

demands

-LRB-

Section

-RRB-

Most

available

biological

network

tools

make

use

of

generic

visualization

packages

-LRB-

and

algorithms

-RRB-

from

the

graph

drawing

community

to

address

problems

of

scalability

However

such

generic

layouts

often

produce

network

drawings

that

resemble

the

infamous

hair

ball

since

the

layout

optimization

criteria

fail

to

take

into

consideration

important

domain-specific

knowledge

such

as

subcellular

localizations

molecular

functions

protein

complexes

or

pathways

-LRB-

see

Fig.

-RRB-

Second

we

lack

robust

query

languages

embedded

directly

within

the

visualization

software

These

querying

languages

allow

users

to

filter

cell-wide

networks

thus

restricting

their

attention

to

a

core

set

of

nodes

of

particular

interest

and

to

organize

this

information

in

an

intuitive

manner

Third

the

increasing

number

of

types

of

interactions

raises

many

practical

and

theoretical

problems

related

to

how

these

datasets

should

be

integrated

into

network

models

how

this

information

is

best

visualized

and

how

the

network

can

be

explored

For

instance

consider

the

visualization

of

a

biological

network

containing

both

physical

and

genetic

interactions

The

fact

that

protein

protein

interactions

occur

between

molecules

that

are

at

least

transiently

physically

co-located

in

the

cell

may

suggest

an

approach

that

seeks

to

generate

a

visualization

that

mimics

cellular

or

organellar

location

However

such

an

approach

may

be

unsuitable

for

genetic

interactions

where

the

interacting

proteins

may

be

implicated

in

two

distinct

distally

located

pathways

Other

types

of

biological

interactions

are

not

necessarily

well

modeled

by

simple

binary

pairwise

graphs

-LRB-

i.e.

-RRB-

Consider

e.g.

expressing

that

a

certain

ternary

complex

forms

if

and

only

if

each

of

the

three

components

of

the

complex

are

present

TOOL

SURVEY

There

are

many

network

visualization

tools

and

each

meets

some

specific

visualization

need

In

fact

during

the

preparation

of

this

manuscript

we

identified

no

less

than

such

tools

and

the

number

continues

to

grow

A

similar

review

-LRB-

Saraiya

et

al.

-RRB-

published

only

a

few

years

ago

identifies

barely

half

that

number

and

due

to

recent

progress

in

this

area

is

silent

on

some

of

what

are

currently

the

most

important

visualization

challenges

We

focus

here

on

six

tools

which

together

contain

many

of

the

important

features

necessary

for

network

visualization

in

a

broad

range

of

studies

A

more

complete

list

can

be

found

in

the

Supplementary

Material

Pathway

Studio

-LRB-

Nikitin

et

al.

-RRB-

-LRB-

formerly

PathwayAssist

-RRB-

is

a

Microsoft

Windows

application

that

combines

an

extensive

set

of

features

with

a

polished

graphic

user

interface

It

includes

most

notably

customizable

network

display

styles

for

assigning

visual

attributes

such

as

node

color

size

and

shape

multi-user

support

subcellular

localization

visualization

and

tight

integration

with

several

database

including

RESNET

-LRB-

-RRB-

KEGG

-LRB-

Kanehisa

-RRB-

BIND

-LRB-

Alfarano

et

al.

-RRB-

GO

-LRB-

Ashburner

et

al.

-RRB-

DIP

-LRB-

Xenarios

et

al.

-RRB-

ERGO

-LRB-

-RRB-

and

PathArt

-LRB-

http

jubilantbiosys.com

-RRB-

Importantly

this

package

also

contains

an

SQL-like

language

that

allows

users

to

query

the

network

using

some

simple

topological

contractions

with

node

and

link

attributes

In

comparison

to

other

packages

this

query

language

provides

a

much

more

flexible

tool

for

quickly

filtering

nodes

of

Tools

for

visually

exploring

biological

networks

interest

from

large

networks

Pathway

Studio

is

a

commercial

product

although

academic

licenses

are

available

Cytoscape

-LRB-

Shannon

et

al.

-RRB-

is

a

well-known

network

visualization

tool

supporting

a

core

set

of

features

including

standard

and

customizable

network

display

styles

ability

to

import

a

large

variety

of

interaction

files

and

zoomable

network

views

Release

claims

a

high-performance

rendering

engine

supporting

networks

with

as

many

as

K

nodes

and

edges

One

of

the

most

positive

aspects

of

Cytoscape

is

the

large

user

and

developer

base

In

particular

since

Cytoscape

is

a

Java

application

whose

source

code

is

released

under

the

Lesser

General

Public

License

-LRB-

LGPL

-RRB-

it

is

straightforward

for

third-party

developers

to

construct

new

plugins

for

the

system

In

fact

more

than

plugins

are

currently

available

available

for

tasks

including

importing

networks

from

various

data

formats

analyzing

networks

and

generating

networks

from

literature

searches

Osprey

-LRB-

Breitkreutz

et

al.

-RRB-

was

one

of

the

first

tools

specifically

designed

to

visualize

and

analyze

large

networks

Consequently

all

but

one

network

layout

in

Osprey

are

elaborations

of

the

circular

layout

-LRB-

described

subsequently

-RRB-

because

they

can

be

quickly

computed

Other

tools

that

specialize

in

large

networks

include

Interviewer

-LRB-

Han

et

al.

-RRB-

and

ProViz

-LRB-

Iragne

et

al.

-RRB-

Osprey

was

also

one

of

the

first

tools

to

support

functional

comparisons

between

different

networks

In

particular

Osprey

is

able

to

superimpose

one

network

additively

on

top

of

another

in

order

to

show

similarities

and

differences

Osprey

is

a

Java

application

and

can

be

used

free

of

charge

The

PATIKA

Project

-LRB-

Demir

et

al.

-RRB-

provides

a

WWWbased

visual

editor

PATIKAweb

for

accessing

a

central

database

containing

pathway

data

from

several

sources

including

Reactome

-LRB-

-RRB-

The

project

is

built

on

top

of

an

extensive

ontology

supporting

representation

of

biological

objects

at

different

levels

of

detail

and

graph

types

that

facilitate

visualizations

of

molecular

complexes

pathways

and

black-box

reactions

PATIKAweb

is

capable

of

producing

high-quality

visualizations

using

the

Tom

Sawyer

Visualization

-LRB-

-RRB-

software

It

also

supports

SQL-like

queries

on

node

and

edge

properties

It

is

implemented

using

Java

Server

Pages

and

is

publicly

available

for

non-profit

use

VisANT

-LRB-

Hu

et

al.

-RRB-

not

only

provides

network

drawing

capabilities

including

support

for

very

large

networks

but

it

is

one

of

the

first

such

packages

to

support

creation

visualization

and

analysis

of

mixed

networks

i.e.

networks

containing

both

directed

and

undirected

links

The

ability

to

use

nodes

to

model

more

complex

entities

such

as

protein

complexes

or

pathways

allow

for

more

informative

visualizations

VisANT

implements

algorithms

for

analyzing

node

degrees

clusters

path

lengths

network

motifs

and

network

randomizations

Like

Cytoscape

VisANT

is

a

Java

application

that

can

be

extended

using

plugins

and

is

freely

available

ProViz

-LRB-

Iragne

et

al.

-RRB-

leverages

the

power

of

the

graph

drawing

package

Tulip

-LRB-

David

-RRB-

for

handling

graphs

containing

millions

of

nodes

and

edges

while

maintaining

a

guaranteed

response

time

-LRB-

i.e.

ProViz

will

never

make

the

user

wait

longer

that

predefined

length

of

time

-RRB-

Tulip

supports

of

a

sophisticated

plugin

architecture

allowing

third-party

developers

to

extend

the

system

Many

plugins

are

currently

available

and

shipped

with

the

system

including

plugins

for

importing/exporting

various

network

file

formats

obtaining

many

different

two

and

three-dimensional

network

layouts

computing

various

network

metrics

like

connectivity

and

eccentricity

and

selecting

subgraphs

such

as

spanning

subtrees

and

paths

between

nodes

ProViz

adds

to

these

features

the

ability

to

import

and

export

PSI-MI

-LRB-

Hermjakob

et

al.

-RRB-

and

IntAct

-LRB-

Hermjakob

et

al.

-RRB-

data

formats

and

interfaces

for

exploring

the

GO

-LRB-

Ashburner

et

al.

-RRB-

and

IntAct

controlled

vocabularies

There

is

functionality

to

define

filters

for

large

networks

Both

ProViz

and

Tulip

are

implemented

in

C++

and

can

be

installed

in

Windows

Linux

or

MacOSX

The

source

code

is

released

under

the

GNU

General

Public

License

-LRB-

GPL

-RRB-

BiologicalNetworks/PathSys

-LRB-

Baitaluk

et

al.

-RRB-

extends

Cytoscape

and

includes

much

of

the

functionality

of

Pathway

Studio

In

fact

Pathway

Studio

users

will

recognize

the

dialog

box

used

to

create

pathways

from

a

set

of

interactions

BiologicalNetworks

is

a

user-interface

to

PathSys

-LRB-

Baitaluk

et

al.

-RRB-

a

graph-based

system

for

creating

a

combined

database

of

biological

pathways

gene

regulatory

networks

and

protein

interaction

maps

It

integrates

over

curated

and

publicly

available

data

sources

including

BIND

-LRB-

Alfarano

et

al.

-RRB-

GO

-LRB-

Ashburner

et

al.

-RRB-

and

KEGG

-LRB-

Kanehisa

-RRB-

for

eight

representative

organisms

PathSys

supports

SQL-like

queries

that

can

explore

network

properties

such

connectivity

and

node

degree

BiologicalNetworks

improves

on

previous

tools

by

better

integrating

expression

data

with

network

visualization

and

analysis

In

particular

after

importing

expression

data

users

can

apply

sorting

normalization

and

clustering

algorithms

on

the

data

and

then

create

various

tables

heat

maps

and

network

views

of

the

data

It

is

implemented

using

Java

Server

Pages

and

is

publicly

available

on

the

WWW

FEATURE

SURVEY

The

utility

of

any

biological

network

visualization

tool

ultimately

depends

on

the

supported

features

In

this

section

we

consider

some

of

the

most

important

features

including

available

network

layout

routines

supported

graphical

notation

integration

of

analysis

into

the

visualization

variety

of

user

input

methods

integration

of

external

biological

data

sources

integration

of

third-party

software

and

finally

availability

-LRB-

licensing

restrictions

and

platform

limitations

-RRB-

Network

layouts

Clearly

one

of

the

most

important

aspects

of

any

biological

network

visualization

is

its

ability

to

automatically

construct

network

drawings

-LRB-

or

layouts

-RRB-

Most

tools

automatically

create

static

layouts

of

networks

using

methods

that

roughly

fall

into

one

of

the

following

categories

Circular

Nearly

every

tool

produces

circular

layouts

In

its

simplest

form

each

node

is

placed

on

the

circumference

of

a

circle

and

links

are

drawn

as

straight-line

segments

between

them

More

complicated

versions

attempt

to

order

the

nodes

to

M.Suderman

and

M.Hallett

uncover

network

symmetries

and

other

versions

place

nodes

on

multiple

concentric

circles

Osprey

-LRB-

Breitkreutz

et

al.

-RRB-

implements

a

total

of

six

different

elaborations

on

the

circular

layout

The

most

complex

called

Spoked

Dual

Ring

creates

circular

layouts

of

highly

connected

parts

of

the

network

inside

a

circle

and

places

the

remaining

vertices

on

the

circle

circumference

The

purpose

of

this

layout

is

to

highlight

the

the

highly

connected

parts

of

the

network

and

show

how

they

relate

to

the

remainder

of

the

network

-LRB-

e.g.

see

Fig

c

in

the

Supplementary

Material

-RRB-

Hierarchical

Directed

edges

are

particularly

important

when

visualizing

regulatory

networks

One

approach

to

visualizing

draws

nodes

on

a

series

of

horizontal

lines

so

that

edges

are

directed

from

nodes

on

lower

horizontal

lines

to

nodes

on

higher

horizontal

lines

This

approach

invented

by

Sugiyama

et

al.

-LRB-

-RRB-

is

included

in

several

systems

including

Pathway

Studio

-LRB-

Nikitin

et

al.

-RRB-

BioPath

-LRB-

Schreiber

-RRB-

ROSPath

-LRB-

Paek

et

al.

-RRB-

CellDesigner

-LRB-

Kitano

-RRB-

and

Virtual

Cell

-LRB-

-RRB-

ProViz

-LRB-

Iragne

et

al.

-RRB-

also

produces

hierarchical

drawings

but

uses

a

different

algorithm

-LRB-

Messinger

et

al.

-RRB-

An

example

from

CellDesigner

is

shown

in

Figure

Force-directed

The

drawings

are

also

known

as

spring

embeddings

-LRB-

Eades

Frick

et

al.

Fruchterman

and

Reingold

Kamada

and

Kawai

-RRB-

since

edges

are

modeled

as

springs

that

pull

linked

nodes

together

or

push

unlinked

nodes

apart

until

the

layout

reaches

a

state

of

equillibrium

-LRB-

e.g.

see

Figure

-RRB-

Force-directed

algorithms

attempt

to

place

nodes

so

that

all

forces

are

in

equillibrium

This

approach

is

quite

popular

because

the

algorithms

are

simple

to

implement

produce

relatively

good

drawings

and

are

easy

tweak

for

specific

applications

In

fact

nearly

every

network

visualization

tool

implements

the

version

described

by

Frick

Sander

and

Wang

-LRB-

Frick

et

al.

-RRB-

The

main

drawback

of

force-directed

algorithms

is

that

they

can

require

a

significant

amount

of

time

before

converging

to

equilibrium

Fortunately

these

algorithms

are

often

easy

to

visually

animate

so

the

user

can

watch

the

network

model

incrementally

approach

equillibrium

and

perhaps

terminate

the

algorithm

when

a

good

drawing

it

obtained

Simulated

annealing

As

the

name

suggests

simulated

annealing

methods

model

problem

space

as

a

set

of

states

each

with

an

associated

energy

so

that

low

energy

states

correspond

to

potential

solutions

Simulated

annealing

algorithms

find

solutions

by

traversing

the

space

moving

from

one

network

layout

to

another

until

it

finds

a

layout

with

sufficiently

low

energy

GeneWays

-LRB-

Rzhetsky

et

al.

-RRB-

generalizes

one

such

algorithm

by

Davidson

and

Harel

-LRB-

Davidson

and

Harel

-RRB-

for

obtaining

layouts

Grid

Layout

-LRB-

Li

and

Kurata

-RRB-

uses

a

related

method

and

shows

how

for

a

yeast

network

their

method

seems

to

spacially

cluster

functionally

related

nodes

The

main

drawback

of

simulated

annealing

algorithms

is

that

they

tend

to

be

slow

-LRB-

even

in

comparison

with

force-directed

methods

-RRB-

Consequently

there

are

practical

limits

on

the

size

of

networks

to

which

they

can

be

applied

All

of

the

above

mentioned

layouts

assume

a

graphical

network

model

where

interactions

are

between

exactly

two

interactors

Although

these

simple

models

have

been

shown

to

yield

biological

insight

they

are

incapable

of

modeling

more

complicated

biological

relationships

that

involve

more

than

two

interactors

like

protein

complexes

relationships

that

depend

on

external

factors

like

cell

state

or

regulatory

circuits

For

instance

although

the

topology

of

the

cis-regulatory

network

describing

the

set

of

transcription

factors

relevant

for

a

particular

promoter

can

be

well

described

the

regulatory

circuit

describing

the

activation

of

the

target

can

not

Unfortunately

extending

current

visualizations

to

handle

this

added

complexity

is

not

straight-forward

Consequently

successful

visualization

depends

on

exploiting

domain-specific

knowledge

to

reduce

difficult

general

problems

to

something

more

manageable

There

have

been

a

few

successful

attempts

in

this

direction

Subcellular

localization

Both

Pathway

Studio

-LRB-

Nikitin

et

al.

-RRB-

and

Patika

-LRB-

Demir

et

al.

-RRB-

are

capable

of

using

localization

to

influence

network

visualizations

if

nodes

are

annotated

with

subcellular

localizations

In

particular

both

systems

partition

the

drawing

space

into

regions

corresponding

to

the

subcellular

localizations

and

then

search

for

layouts

where

nodes

are

forcibly

constrained

to

their

respective

locations

Both

systems

make

use

of

modified

force-directed

algorithms

to

achieve

this

Pathway

Studio

uses

representative

cartoons

as

backgrounds

for

each

region

in

order

to

improve

readability

Two

other

tools

Cell

Illustrator

-LRB-

Nagasaki

et

al.

-RRB-

and

Cerebral

-LRB-

Barsky

et

al.

-RRB-

support

subcellular

localization

in

drawings

where

nodes

are

restricted

to

positions

on

a

grid

For

an

example

see

Figure

Composite

nodes

Pathway

Studio

-LRB-

Nikitin

et

al.

-RRB-

Patika

-LRB-

Demir

et

al.

-RRB-

and

VisANT

-LRB-

Hu

et

al.

-RRB-

visualize

composite

nodes

representing

molecular

complexes

and

pathways

as

single

nodes

that

can

be

interactively

expanded

to

show

individual

members

or

collapsed

Some

improvement

to

this

functionality

is

warranted

VisANT

e.g.

expands

composite

nodes

by

creating

a

small

window

on

top

of

the

current

network

view

and

then

draws

the

composite

node

members

inside

the

window

Unfortunately

these

windows

cover

nearby

neighbors

making

it

difficult

to

see

how

the

composite

node

members

relate

to

the

greater

network

When

the

user

attempts

to

expand

a

node

in

Patika

-LRB-

e.g.

see

Fig

-RRB-

there

must

be

space

for

the

node

to

be

expanded

in

the

drawing

otherwise

Patika

will

not

expand

the

node

Hierarchical

clusters

Hierarchical

clusters

of

the

nodes

or

edges

can

be

very

useful

for

obtaining

simplified

visualizations

of

large

complex

networks

Schwikowski

et

al.

-LRB-

-RRB-

show

three

views

of

a

yeast

protein

protein

interaction

network

either

display

all

nodes

and

edges

without

any

graph

drawing

constraints

collapse

nodes

of

the

same

function

into

composite

nodes

or

collapse

proteins

of

the

same

subcellular

localization

into

composite

nodes

Similar

to

quotient

graphs

-LRB-

e.g.

Bourqui

et

al.

-RRB-

an

edge

between

two

composite

nodes

in

the

last

two

views

indicates

the

existence

of

a

threshhold

number

of

edges

between

composite

node

members

More

general

hierarchical

clusters

of

proteins

exist

including

e.g.

the

Structural

Classification

of

Proteins

-LRB-

SCOP

-RRB-

-LRB-

Murzin

et

al.

-RRB-

and

Gene

Ontology

-LRB-

Ashburner

et

al.

-RRB-

Tools

for

visually

exploring

biological

networks

The

SCOP

hierarchy

contains

several

levels

of

protein

domain

similarity

differentiated

by

increasing

specificity

class

fold

superfamily

family

protein

and

species

Lappe

et

al.

-LRB-

-RRB-

show

that

a

large

yeast

protein-protein

interaction

network

at

the

SCOP

level

of

superfamily

has

a

very

simple

drawing

Edge

thickness

between

superfamilies

indicates

the

number

of

links

between

proteins

in

each

superfamily

A

recent

Cytoscape

plugin

GenePro

-LRB-

Vlasblom

et

al.

-RRB-

partially

supports

interactive

exploration

of

hierarchical

network

clusters

-LRB-

e.g.

see

Fig

-RRB-

Given

a

protein

interaction

network

and

a

predefined

clustering

on

its

nodes

GenePro

initially

presents

the

user

with

the

most

abstract

view

and

then

allows

the

user

to

expand

clusters

in

a

new

window

to

see

cluster

members

GenePro

can

also

render

nodes

as

pie

charts

showing

the

fractions

of

proteins

sharing

a

common

feature

Time

series

Most

tools

do

not

attempt

to

visualize

time

series

data

Those

that

do

e.g.

BioTapestry

-LRB-

Longabaugh

et

al.

-RRB-

simply

highlight

links

and

nodes

that

are

active

during

a

given

time

period

but

otherwise

present

a

static

picture

Unfortunately

this

approach

fails

to

take

advantage

of

the

reduced

network

size

at

each

time

point

and

the

small

number

of

changes

from

one

time

point

to

the

next

Neighbor

expansion

Rather

than

show

the

entire

network

in

one

display

many

tools

including

VisANT

-LRB-

Hu

et

al.

-RRB-

Osprey

-LRB-

Breitkreutz

et

al.

-RRB-

and

MINT

Viewer

-LRB-

Zanzoni

et

al.

Chatr-aryamontri

et

al.

-RRB-

initially

show

a

small

subnetwork

and

then

allow

the

user

to

click

on

a

node

in

order

to

add

all

of

its

neighbors

to

the

current

view

-LRB-

e.g.

see

Fig

-RRB-

Later

the

user

may

click

the

node

again

to

hide

those

neighbors

The

best

tools

provide

animations

from

one

network

view

to

another

so

that

the

user

can

easily

maintain

a

mental

mapping

from

the

previous

to

the

current

view

Three

dimensions

Currently

only

a

few

tools

including

GeneWays

-LRB-

Rzhetsky

et

al.

-RRB-

and

ProViz

-LRB-

Iragne

et

al.

-RRB-

offer

three-dimensional

visualizations

and

these

are

all

very

rudimentary

Making

use

of

higher

dimensions

is

difficult

because

users

are

generally

viewing

the

network

on

a

two-dimensional

screen

Consequently

dynamic

navigation

is

a

necessity

and

is

often

awkward

without

specialized

equipment

In

addition

graphical

complexity

increases

because

network

entities

must

not

only

represent

data

but

also

simulate

distance

from

the

user

Matrix

representations

Although

networks

are

typically

visualized

using

the

so-called

ball-and-stick

representation

a

complementary

representation

uses

adjacency

matrices

and

is

often

useful

for

uncovering

topological

patterns

in

e.g.

dense

networks

In

Bioinformatics

these

are

more

commonly

called

clustergrams

heatmaps

with

dendrograms

or

cluster

maps

Here

each

node

corresponds

to

exactly

one

row

and

one

column

in

the

matrix

and

the

intersection

of

a

row

and

column

is

colored

to

represent

the

existence

or

strength

of

the

link

between

the

corresponding

nodes

Drawing

this

matrix

with

different

row

and

column

orderings

can

uncover

monochromatic

patches

indicating

clusters

of

nodes

with

similar

behavior

-LRB-

e.g.

Fig.

of

Collins

et

al.

-RRB-

To

create

a

matrix

representation

researchers

generally

use

one

or

more

methods

for

optimally

ordering

rows

and

columns

provided

by

their

preferred

statistical

software

package

Unfortunately

generalpurpose

orderings

are

rarely

sufficient

for

specific

biological

applications

so

there

has

been

some

recent

investigation

of

support

for

computing

user-assisted

orderings

-LRB-

e.g.

Henry

and

Fekete

-RRB-

Graphical

notation

Until

recently

most

tools

relied

heavily

on

node/edge

shape

-LRB-

e.g.

to

represent

whether

the

node

corresponds

to

a

protein

gene

or

small

molecule

-RRB-

and

colors

-LRB-

e.g.

gene

expression

subcellular

localization

molecular

function

-RRB-

to

visualize

network

properties

Unfortunately

the

lack

of

standards

describing

typical

biological

objects

results

in

the

need

for

each

visualization

to

come

equipped

with

a

legend

describing

symbols

and

colors

In

some

cases

the

use

of

such

node

and

edge

attributes

actually

detracts

from

readability

This

has

motivated

the

development

of

informal

standards

that

have

evolved

via

imitation

and

the

popularity

of

some

tools

For

example

elements

of

the

Pathway

Studio

layout

style

has

migrated

into

the

BiologicalNetworks

package

However

concern

about

a

lack

of

formal

standards

has

been

increasing

-LRB-

Klipp

et

al.

-RRB-

and

there

now

exist

several

efforts

towards

a

fully

standardized

graphics

vocabulary

-LRB-

Cook

et

al.

Kitano

Kohn

Kohn

et

al.

Kurata

et

al.

Pirson

et

al.

-RRB-

Cook

et

al.

-LRB-

-RRB-

outline

a

carefully

reasoned

approach

that

can

describe

complex

biological

systems

unambiguously

The

authors

hope

that

such

a

standard

would

be

amenable

to

various

automations

including

simulation

and

resolution

of

queries

like

Find

models

where

a

potassium

channel

blocker

affects

cell-cycle

progression

Unfortunately

according

to

Kohn

et

al.

-LRB-

-RRB-

this

proposal

is

too

cumbersome

for

biologists

to

actually

use

so

they

propose

their

own

molecular

interaction

map

-LRB-

MIM

-RRB-

notation

for

regulatory

networks

that

deliberately

permits

some

forms

of

ambiguity

For

example

in

the

interest

of

visual

compactness

MIM

diagrams

do

not

specify

an

order

for

steps

in

a

reaction

Kohn

et

al.

-LRB-

-RRB-

downplay

concerns

for

automation

claiming

that

the

process

of

manually

creating

MIM

diagrams

is

in

itself

and

important

tool

for

exposing

gaps

in

our

knowledge

of

biological

processes

Others

however

-LRB-

Kurata

et

al.

-RRB-

have

implemented

a

tool

called

CADLIVE

that

supports

a

slight

modification

of

MIM

Kitano

-LRB-

-RRB-

defines

an

alternative

graphical

notation

called

Systems

Biology

Graphical

Notation

-LRB-

SBGN

-RRB-

that

uses

state-transition

diagrams

to

model

regulatory

networks

In

constrast

to

MIM

diagrams

SBGN

diagrams

do

enforce

an

event

sequence

and

are

consequently

less

compact

because

they

may

contain

multiple

nodes

for

a

single

molecule

In

addition

they

have

implemented

a

tool

called

CellDesigner

that

generates

SBGN

diagrams

-LRB-

Funahashi

et

al.

-RRB-

as

well

as

created

a

website

-LRB-

-RRB-

in

order

to

promote

wider

community

use

and

development

of

SBGN

Figure

shows

an

example

from

CellDesigner

Integration

of

analysis

There

are

currently

few

network

tools

designed

for

both

the

visualization

of

biological

networks

and

the

analysis

of

these

M.Suderman

and

M.Hallett

networks

Consequently

users

are

forced

to

switch

between

tools

resulting

in

the

need

to

continually

import/export

and

reformat

data

Clearly

a

more

optimal

choice

would

be

a

network

tool

that

supports

both

visualization

and

analysis

with

a

seamless

integration

between

these

two

procedures

BiologicalNetworks

is

an

early

leader

here

allowing

pathway

visualizations

from

queries

that

combine

gene

expression

data

analysis

with

simple

topological

patterns

Several

specialized

tools

exist

for

the

integration

of

very

specific

types

of

visualization

and

data

Wilmascope

-LRB-

Dwyer

et

al.

-RRB-

shows

time

series

data

using

dimensional

layouts

-LRB-

networks

are

stacked

one

on

top

of

the

other

by

time

points

in

order

to

highlight

changes

in

the

network

-RRB-

MAVisto

-LRB-

Schreiber

and

Schwobbermeyer

-RRB-

searches

for

over-represented

network

motifs

and

highlights

these

in

drawings

of

the

network

GridLayout

-LRB-

Li

and

Kurata

-RRB-

highlights

functionally

related

nodes

by

placing

them

in

roughly

the

same

regions

of

the

drawing

A

large

number

of

tools

-LRB-

Dahlquist

et

al.

Grosu

et

al.

Karp

et

al.

Khatri

et

al.

Mlecnik

et

al.

Pan

et

al.

Thimm

et

al.

-RRB-

are

designed

mainly

for

mapping

gene

or

protein

expression

profiles

onto

existing

network

diagrams

library

Currently

GUESS

is

more

widely

used

in

the

social

network

community

but

has

been

used

for

biological

networks

To

the

best

of

our

knowledge

only

Pathway

Studio

supports

a

multi-user

environment

that

allows

users

to

set

sharing

permissions

on

data

and

computed

results

Incorporation

of

external

data

sources

User

input

and

customization

Most

of

the

major

tools

support

a

graphic

user

interface

where

mouse

clicks/movements

simple

dialog

boxes

and

data

imports

allow

most

functionality

to

be

accessed

However

in

many

cases

the

functionality

offered

by

the

biological

network

tool

is

insufficient

or

cumbersome

for

the

task

at

hand

Several

tools

offer

various

means

for

third

parties

to

develop

new

functionality

and

integrate

it

directly

within

the

tool

Plugins

are

an

important

way

for

advanced

users

to

customize

and

extend

an

application

Of

the

major

tools

Cytoscape

VisANT

and

ProViz

support

plugins

In

fact

all

three

of

these

tools

are

based

on

a

somewhat

generic

software

design

in

order

to

stimulate

a

community

of

third

party

develops

capable

of

expanding

their

system

to

address

a

greater

range

of

biological

applications

Plugins

require

however

a

significant

amount

of

effort

and

expertise

to

create

and

thus

do

not

represent

a

feasible

avenue

for

tailoring

systems

to

meet

the

needs

of

a

particular

scientific

application

for

most

labs

Scripting

and

query

languages

can

provide

a

convenient

trade-off

between

the

power

and

flexibility

of

plugins

whilst

conserving

the

convenience

of

features

available

through

graphic

user

interfaces

Currently

Pathway

Studio

offers

a

wizard

interface

for

creating

very

simple

network

and

data

queries

and

only

BiologicalNetworks

provides

a

language

interface

for

expressing

such

queries

A

recent

tool

GUESS

-LRB-

Adar

-RRB-

provides

the

user

with

a

powerful

scripting

language

based

on

Python

-LRB-

-RRB-

that

includes

a

convenient

notation

for

networks

The

GUESS

user

interface

consists

of

a

component

for

network

visualization

and

a

component

for

entering

commands

in

the

scripting

language

The

language

allows

for

the

analysis

and

modification

of

network

layouts

however

it

is

sufficiently

simple

that

users

do

not

require

previous

programming

experience

In

addition

it

is

relatively

easy

to

extend

its

functionality

by

creating

interfaces

to

other

libraries

such

as

the

R

statistical

Most

of

the

biological

network

tools

are

distributed

with

preformated

versions

of

popular

interaction

databases

-LSB-

e.g.

BIND

-LRB-

Alfarano

et

al.

-RRB-

and

DIP

-LRB-

Xenarios

et

al.

-RRB-

-RSB-

metabolic

pathway

databases

-LSB-

e.g.

KEGG

-LRB-

Kanehisa

-RRB-

-RSB-

gene

ontologies

-LSB-

GO

-LRB-

Ashburner

et

al.

-RRB-

-RSB-

and

molecular

sequence

databases

-LSB-

e.g.

UniProt

-LRB-

Bairoch

et

al.

-RRB-

Entrez

Gene

fcgi?db

gene

-RSB-

There

are

of

course

many

additional

bioinformatics

public

repositories

and

datasets

produced

by

individual

labs

that

may

be

relevant

to

the

user

's

current

study

The

translation

of

such

datasets

into

the

format

required

by

the

specific

network

tool

is

in

many

cases

non-trivial

especially

for

laboratories

without

significant

bioinformatics

expertise

Partial

solutions

to

this

ubiquitous

problem

can

be

found

in

several

biological

network

tools

In

VisANT

Pathway

Studio

and

BiologicalNetworks

all

data

are

ported

to

a

central

repository

and

represented

in

an

application-specific

format

This

forces

the

end-user

to

build

parsers

for

each

new

data

resource

and

reformat

the

data

to

the

precise

format

expected

by

the

tool

This

also

requires

that

the

end-users

rely

on

developers

to

maintain

up-to-date

versions

of

each

imported

database

Fortunately

a

small

number

of

data

format

standards

are

becoming

wide

spread

When

both

the

dataset

is

expressed

in

a

standardized

format

and

the

network

visualizaton

tool

supports

the

importation

of

this

format

the

incorporation

of

these

datasets

requires

neither

programming

expertise

nor

an

understanding

of

the

format

itself

The

three

most

popular

examples

SBML

PSI-MI

and

BioPAX

are

open

XML-based

-LRB-

-RRB-

standards

that

use

a

leveled

approach

meaning

that

each

standard

is

described

at

various

levels

of

complexity

and

specificity

Users

may

then

choose

the

simplest

level

sufficient

to

represent

all

of

the

necessary

information

in

their

dataset

The

SBML

-LRB-

Systems

Biology

Markup

Language

-RRB-

-LRB-

Hucka

and

Finney

Hucka

et

al.

-RRB-

is

supported

by

over

software

systems

including

Cytoscape

PATIKA

and

BiologicalNetworks

SBML

is

designed

for

modeling

biochemical

reaction

networks

at

a

level

that

admits

automated

simulation

Currently

SBML

levels

and

are

defined

but

a

third

more

detailed

level

is

planned

that

would

support

the

composition

of

models

from

component

submodels

rule-based

generation

of

states

and

interactions

and

descriptions

of

cell

geometries

-LRB-

Hucka

and

Finney

-RRB-

Unlike

SBML

PSI-MI

-LRB-

Proteomics

Standards

InitiativeMolecular

Interactions

-RRB-

-LRB-

Hermjakob

et

al.

-RRB-

has

the

more

pragmatic

mandate

of

describing

molecular

interactions

rather

than

complete

cellular

models

Several

visualization

tools

including

Cytoscape

and

VisANT

can

import

and

export

PSIMI

formated

data

and

several

public

databases

accept

submissions

in

PSI-MI

format

Tools

for

visually

exploring

biological

networks

A

third

standard

BioPAX

-RRB-

has

released

two

levels

Level

is

used

for

representing

metabolic

pathways

while

level

is

for

representing

molecular

interactions

PATIKA

can

export

data

in

BioPAX

format

whereas

VisANT

and

Cytoscape

-LRB-

using

the

BioPAX

plugin

-RRB-

can

import

BioPAX

files

In

a

comparative

study

of

PSI-MI

SBML

and

BioPAX

Stromback

and

Lambrix

-LRB-

-RRB-

claim

that

BioPAX

is

the

most

general

and

expressive

of

the

three

while

PSI-MI

is

most

appropriate

for

describing

molecular

interactions

and

SBML

is

best

suited

for

simulation

models

of

molecular

pathways

Integration

of

third-party

software

Like

external

datasources

interfacing

with

external

software

tools

can

be

very

difficult

or

even

impossible

However

overcoming

this

hurdle

is

key

to

contributing

to

the

trend

toward

integrated

visualization

and

analysis

There

have

been

a

few

early

attempts

For

example

GUESS

-LRB-

described

earlier

-RRB-

contains

a

nearly

transparent

interface

to

the

R

statistical

package

-LRB-

-RRB-

in

its

scripting

language

Use

of

the

CytoTalk

plugin

for

Cytoscape

is

a

quick

way

to

add

network

visualization

facilities

to

analysis

software

The

plugin

simply

allows

external

processes

-LRB-

including

e.g.

R

Python

and

Perl

-RRB-

to

remotely

manipulate

networks

displayed

in

Cytoscape

DISCUSSION

High-throughput

experimental

biology

has

already

managed

to

create

nearly

cell-wide

maps

of

protein

protein

protein

nucleotide

and

genetic

interactions

As

the

cost

efficiency

and

accuracy

of

these

assays

improve

we

can

expect

datasets

several

orders

of

magnitude

larger

than

we

currently

have

at

our

disposal

over

a

greater

number

of

organisms

Furthermore

the

variety

of

data

types

will

also

increase

to

include

e.g.

a

greater

emphasis

on

protein-small

molecule

interactions

more

comprehensive

time-series

gene

and

protein

expression

data

and

more

comprehensive

genetic

interaction

studies

There

is

a

natural

trend

towards

combining

this

information

within

probabilistic

or

dynamical

models

capable

of

capturing

the

most

salient

aspects

of

complex

biological

processes

In

order

to

extract

as

much

information

as

we

can

from

these

meta-datasets

current

biological

network

tools

must

evolve

in

functionality

to

address

issues

of

scalability

integration

and

visualization

With

respect

to

scalability

visualization

tools

will

need

to

employ

novel

approaches

to

providing

access

to

massive

datasets

while

respecting

end-user

limitations

More

than

ever

before

users

will

depend

on

sophisticated

network

analysis

algorithms

to

uncover

interesting

biological

stories

that

they

used

to

find

by

eye

in

much

smaller

networks

There

are

several

positive

examples

in

this

direction

Given

a

pathway

in

some

organism

PathBLAST

is

able

to

identify

evolutionarily

conserved

pathways

in

a

second

organism

by

solving

a

restricted

version

of

the

subgraph

isomorphism

problem

Given

a

list

of

distinguished

genes

or

gene

products

corresponding

to

e.g.

differentially

expressed

genes

from

a

microarray

experiment

the

Steiner

approach

of

Scott

et

al.

-LRB-

-RRB-

searches

large

interaction

networks

for

the

minimal

connecting

set

of

nodes

that

hook

up

the

members

of

the

input

list

The

resultant

subnetwork

is

typically

of

a

size

that

is

more

amenable

to

visualization

and

analysis

Unfortunately

the

size

of

cell-wide

interaction

networks

renders

many

network

analysis

problems

intractable

For

instance

MAVisto

-LRB-

Schreiber

and

Schwobbermeyer

-RRB-

determines

whether

or

not

certain

motifs

are

over-represented

in

a

network

Since

determining

the

presence

of

a

motif

is

equivalent

to

solving

the

subgraph

isomorphism

problem

it

is

very

unlikely

that

any

computer

will

ever

be

able

to

search

for

motifs

of

more

than

a

few

nodes

in

any

cell-wide

interaction

network

Fortunately

the

computational

intractability

of

a

problem

in

general

does

not

imply

intractability

in

a

specific

biological

context

Creatively

rephrasing

the

question

can

lead

to

tractable

solutions

e.g.

by

restricting

attention

to

the

Steiner

tree

rather

than

the

original

network

or

to

a

single

cell

state

or

location

With

respect

to

integration

visualization

tools

will

need

to

go

beyond

simplistic

graphical

models

and

mere

compliance

with

accepted

standards

in

order

to

truly

integrate

new

data

types

Whereas

interaction

network

models

assume

a

static

list

of

interacting

pairs

there

are

many

examples

of

proteins

whose

function

and

therefore

interaction

types

and

partners

differ

depending

on

cell

state

or

subcellular

location

For

example

in

the

absence

of

glucocorticoids

the

glucocorticoid

receptor

-LRB-

GR

-RRB-

is

bound

to

the

cytosolic

chaperone

in

the

cytosol

The

introduction

of

glucocorticoids

causes

the

release

of

GR

from

and

its

subsequent

retrotranslocation

into

the

nucleus

where

it

functions

either

as

a

transcription

factor

-LRB-

protein

nucleotide

interactions

-RRB-

or

an

adapter

protein

within

large

transcriptional

complexes

-LRB-

protein

protein

interactions

-RRB-

We

are

not

yet

however

close

to

obtaining

complete

information

about

cell

state

so

models

will

need

to

strike

a

delicate

balance

between

being

immediately

useful

and

attempting

to

model

true

biological

networks

Standards

like

SBML

-LRB-

Hucka

and

Finney

Hucka

et

al.

-RSB-

that

aim

at

completeness

can

be

used

to

measure

progress

With

respect

to

visualization

single

network

views

will

provide

little

more

than

brief

glimpses

of

the

large

datasets

Visualization

tools

will

then

need

to

support

many

different

types

of

views

each

network

view

at

a

different

level

of

detail

Dynamic

navigation

from

one

view

to

another

will

be

a

key

to

showing

the

connection

between

different

views

Navigating

from

one

time

series

point

to

another

for

instance

could

involve

a

view

showing

only

the

differences

between

the

two

time

points

If

the

time

points

are

consecutive

the

number

of

differences

will

tend

to

be

quite

small

A

similar

approach

could

be

applied

to

subcellular

localization

information

as

well

To

adequately

address

each

of

these

issues

active

cooperation

will

be

required

between

a

variety

research

fields

including

graph

drawing

information

visualization

network

analysis

and

of

course

biology

It

is

unfortunate

to

witness

the

number

of

new

tools

designed

for

biology

that

have

earlier

analogs

in

other

research

areas

Pajek

-LRB-

Batagelj

and

Mrvar

-RRB-

for

instance

is

a

general

visualization

and

analysis

tool

that

has

been

used

extensively

to

study

social

networks

but

has

seen

limited

use

with

biological

networks

-LSB-

Ho

et

al.

Ludemann

et

al.

Tong

et

al.

-RSB-

This

waste

of

resources

is

caused

by

a

simple

lack

of

communication

between

M.Suderman

and

M.Hallett

fields

For

instance

graph

drawing

researchers

ignorant

of

emerging

biological

models

tend

to

tackle

layout

problems

of

little

use

in

biology

or

at

best

express

solutions

to

relevant

problems

in

a

way

that

is

inaccessible

to

biologists

A

biologically

informed

graph

drawing

community

however

would

be

capable

of

generating

clever

ideas

for

optimization

criteria

that

produce

layouts

more

easily

interpreted

by

biologists

This

would

have

tremendous

benefits

by

better

facilitating

the

evaluation

of

correctness

of

existing

interaction

networks

Consider

e.g.

a

visualization

of

protein-protein

interactions

where

proteins

are

placed

in

the

layout

according

to

subcellular

localizations

Edges

that

span

any

membrane

would

be

identified

as

potential

mistakes

either

because

they

are

false

positives

identified

by

the

assay

or

because

the

subcellular

location

may

be

improperly

or

incompletely

described

in

the

dataset

The

key

to

success

of

this

endeavor

is

the

involvement

of

all

these

communities

towards

standards

open

access

software

and

distributed

development

Tools

developed

in

this

environment

will

attract

large

user

communities

avoid

duplication

of

effort

and

ultimately

lead

the

way

towards

the

goals

of

systems

biology

ACKNOWLEDGEMENT

Funding

was

provided

by

NSERC

-LRB-

Postdoctoral

Fellowship

to

M.S.

Discovery

to

M.H.

-RRB-

Conflict

of

Interest

declared

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In

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the

SIGCHI

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Factors

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Computing

Systems

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Press

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pp.

ISBN

Albert

R.

et

al.

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Error

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tolerance

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complex

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Nature

Alfarano

C.

et

al.

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-RRB-

The

biomolecular

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Nucleic

Acids

Res.

Ashburner

M.

et

al.

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Gene

ontology

tool

for

the

unification

of

biology

the

Gene

Ontology

Consortium

Nat

Genet.

Bairoch

A.

et

al.

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The

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uniprot

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Nucleic

Acids

Res.

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Suppl

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Baitaluk

M.

et

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BiologicalNetworks

visualization

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systems

biology

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M.

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systems

biology

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Barsky

A.

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