REVIEWS

Network

motifs

theory

and

experimental

approaches

Uri

Alon

Abstract

Transcription

regulation

networks

control

the

expression

of

genes

The

transcription

networks

of

well-studied

microorganisms

appear

to

be

made

up

of

a

small

set

of

recurring

regulation

patterns

called

network

motifs

The

same

network

motifs

have

recently

been

found

in

diverse

organisms

from

bacteria

to

humans

suggesting

that

they

serve

as

basic

building

blocks

of

transcription

networks

Here

I

review

network

motifs

and

their

functions

with

an

emphasis

on

experimental

studies

Network

motifs

in

other

biological

networks

are

also

mentioned

including

signalling

and

neuronal

networks

Transcription

regulation

networks

describe

the

interactions

between

transcription

factor

proteins

and

the

genes

that

they

Transcription

factors

respond

to

biological

signals

and

accordingly

change

the

transcription

rate

of

genes

allowing

cells

to

make

the

proteins

they

need

at

the

appropriate

times

and

amounts

Recent

work

indicates

that

transcription

networks

contain

a

small

set

of

recurring

regulation

patterns

called

network

motifs

Network

motifs

can

be

thought

of

as

recurring

circuits

of

interactions

from

which

the

networks

are

built

Network

motifs

were

first

systematically

defined

in

Escherichia

coli

in

which

they

were

detected

as

patterns

that

occurred

in

the

transcription

network

much

more

often

than

would

be

expected

in

random

networks

The

same

motifs

have

since

been

found

in

organisms

from

and

to

plants

and

This

Review

focuses

on

experimental

studies

of

network

motifs

a

comprehensive

treatment

with

quantitative

models

can

be

found

in

REF

The

Review

discusses

two

types

of

transcription

network

sensory

networks

that

respond

to

signals

such

as

stresses

and

nutrients

and

developmental

networks

that

guide

differentiation

events

I

will

first

consider

sensory

networks

the

motifs

of

which

are

common

to

both

types

of

network

I

will

then

turn

to

motifs

that

are

specific

to

developmental

networks

This

Review

focuses

on

transcription

networks

because

they

are

the

most

studied

Network

motifs

are

also

found

in

other

biological

networks

such

as

those

that

involve

protein

modifications

or

interactions

between

neuronal

cells

I

will

briefly

describe

the

motifs

that

are

found

in

these

biological

networks

The

main

idea

that

is

presented

in

this

Review

is

that

each

network

motif

can

carry

out

specific

information-processing

functions

These

functions

have

been

analysed

using

mathematical

models

and

tested

with

dynamic

experiments

in

living

cells

Still

there

is

much

to

be

done

it

is

important

to

further

experimentally

test

the

functions

that

each

network

motif

can

perform

Such

experiments

could

illuminate

the

dynamics

of

the

many

systems

in

which

each

motif

appears

Furthermore

it

is

important

to

test

whether

motifs

can

help

us

to

understand

the

densely

connected

networks

of

higher

organisms

Department

of

Molecular

Cell

Biology

Weizmann

Institute

of

Science

Rehovot

Israel

e-mail

urialon@weizmann.ac.il

doi

Simple

regulation

Let

's

begin

by

understanding

the

dynamics

of

a

basic

transcription

interaction

a

single

arrow

in

the

network

which

is

referred

to

here

as

simple

regulation

-LRB-

FIG.

-RRB-

Simple

regulation

can

serve

as

a

reference

for

understanding

the

dynamic

functions

of

network

motifs

Simple

regulation

occurs

when

transcription

factor

Y

regulates

gene

X

with

no

additional

interactions

-LRB-

FIG.

-RRB-

Y

is

usually

activated

by

a

signal

Sy

The

signal

can

be

an

inducer

molecule

that

directly

binds

Y

or

a

modification

of

Y

by

a

signal-transduction

cascade

and

so

on

When

transcription

begins

the

concentration

of

the

gene

product

X

rises

and

converges

to

a

steady-state

level

-LRB-

FIG.

-RRB-

This

level

is

equal

to

the

ratio

of

the

production

and

degradation

rates

where

degradation

includes

both

active

degradation

and

the

effect

of

dilution

by

cell

growth

When

production

stops

the

concentration

of

the

gene

product

decays

exponentially

In

both

cases

the

response

time

which

is

defined

as

the

time

it

takes

to

reach

halfway

between

the

initial

and

final

levels

is

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equal

to

the

half-life

of

the

gene

The

faster

the

degradation

rate

the

shorter

the

response

time

For

proteins

that

are

not

actively

degraded

as

is

the

case

for

most

proteins

in

growing

bacterial

cells

the

response

time

is

equal

to

one

cell-generation

time

This

is

a

result

of

the

dilution

effect

from

cell

growth

Negative

autoregulation

Negative

autoregulation

-LRB-

NAR

-RRB-

occurs

when

a

transcription

factor

represses

the

transcription

of

its

own

-LRB-

FIG.

-RRB-

This

network

motif

occurs

in

about

half

of

the

repressors

in

E.

coli

and

in

many

eukaryotic

NAR

has

been

shown

to

display

two

important

functions

First

NAR

speeds

up

the

response

time

of

gene

circuits

This

occurs

when

NAR

uses

a

strong

promoter

to

obtain

a

rapid

initial

rise

in

the

concentration

of

protein

X

When

X

concentration

reaches

the

repression

threshold

for

its

own

promoter

the

production

rate

of

new

X

decreases

Thus

the

concentration

of

X

locks

into

a

steady-state

level

that

is

close

to

its

repression

threshold

This

steady-state

level

can

be

adjusted

over

evolutionary

time

by

mutations

that

cause

variation

in

the

repression

threshold

of

X

to

its

own

promoter

By

contrast

a

simply

regulated

gene

that

is

designed

to

reach

the

same

steady-state

level

must

use

a

weaker

promoter

As

a

result

an

NAR

system

reaches

of

its

steady

state

faster

than

a

simply

regulated

gene

-LRB-

FIG.

-RRB-

The

dynamics

of

NAR

show

a

rapid

initial

rise

followed

by

a

sudden

locking

into

the

steady

state

possibly

accompanied

by

an

overshoot

or

damped

oscillations

Response

acceleration

-LRB-

or

speed-up

-RRB-

by

NAR

has

been

demonstrated

experimentally

using

a

fluorescently

tagged

repressor

TetR

that

was

designed

to

repress

its

own

promoter

-LRB-

FIG.

-RRB-

Speed-up

in

a

natural

context

was

demonstrated

in

the

SOS

DNA-repair

system

of

E.

coli

in

which

the

master

regulator

LexA

represses

its

own

These

and

many

of

the

other

experiments

discussed

in

this

Review

were

made

possible

by

fluorescent-reporter

assays

which

allow

the

transcription

dynamics

of

living

cells

to

be

measured

with

high

resolution

and

accuracy

In

addition

to

speeding

responses

NAR

can

reduce

cell

cell

variation

in

protein

levels

These

variations

are

due

to

an

inherent

source

of

noise

the

production

rates

of

proteins

fluctuate

by

tens

of

percents

-LRB-

reviewed

in

REF

-RRB-

-LRB-

FIG.

-RRB-

This

noise

results

in

cell

cell

variation

in

protein

level

NAR

can

in

many

cases

reduce

these

variations

high

concentrations

of

X

reduce

its

own

rate

of

production

whereas

low

concentrations

cause

an

increased

production

rate

The

result

is

a

narrower

distribution

of

protein

levels

than

would

be

expected

in

simply

regulated

genes

-LRB-

FIG.

-RRB-

as

demonstrated

experimentally

by

Besckei

and

However

if

the

NAR

feedback

contains

a

long

delay

noise

can

also

be

amplified

Positive

autoregulation

Positive

autoregulation

-LRB-

PAR

-RRB-

occurs

when

a

transcription

factor

enhances

its

own

rate

of

production

-LRB-

FIG.

-RRB-

The

effects

are

opposite

to

those

of

NAR

response

times

are

slowed

and

variation

is

usually

enhanced

a

Y

X

b

X

c

X

d

X/Xst

Cell

generations

e

X/Xst

Cell

generations

f

Number

of

cells

log

X

Negative

autoregulation

Simple

regulation

Positive

autoregulation

Figure

Simple

regulation

and

autoregulation

a

In

simple

regulation

transcription

factor

Y

is

activated

by

a

signal

Sy

When

active

it

binds

the

promoter

of

gene

X

to

enhance

or

inhibit

its

transcription

rate

b

In

negative

autoregulation

-LRB-

NAR

-RRB-

X

is

a

transcription

factor

that

represses

its

own

promoter

c

In

positive

autoregulation

-LRB-

PAR

-RRB-

X

activates

its

own

promoter

d

NAR

speeds

the

response

time

-LRB-

the

time

needed

to

reach

halfway

to

the

steady-state

concentration

-RRB-

relative

to

a

simple-regulation

system

that

reaches

the

same

steady-state

expression

PAR

slows

the

response

time

e

An

experimental

study

of

NAR

using

a

synthetic

gene

circuit

in

which

the

repressor

TetR

fused

to

GFP

represses

its

own

promoter

High-resolution

fluorescence

measurements

in

living

Escherichia

coli

cells

show

that

this

NAR

motif

has

a

response

time

about

fivefold

faster

than

a

simple-regulation

design

f

A

schematic

cell

cell

distribution

of

protein

levels

NAR

tends

to

make

this

distribution

narrower

in

comparison

with

simple

regulation

whereas

PAR

tends

to

make

it

wider

and

in

extreme

cases

bimodal

with

two

populations

of

cells

X/Xst

X

concentration

relative

to

steady

state

Xst

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PAR

slows

the

response

time

because

at

early

stages

when

levels

of

X

are

low

production

is

slow

Production

picks

up

only

when

X

concentration

approaches

the

activation

threshold

for

its

own

promoter

Thus

the

desired

steady

state

is

reached

in

an

S-shaped

curve

-LRB-

FIG.

-RRB-

The

response

time

is

longer

than

in

a

corresponding

simple-regulation

system

as

shown

and

experimentally

by

Maeda

and

PAR

tends

to

increase

cell

cell

variability

If

PAR

is

weak

-LRB-

that

is

X

moderately

enhances

its

own

production

rate

-RRB-

the

cell

cell

distribution

of

X

concentration

is

expected

to

be

broader

than

in

the

case

of

a

simply

regulated

gene

-LRB-

FIG.

-RRB-

Strong

PAR

can

lead

to

bimodal

distributions

whereby

the

concentration

of

X

is

low

in

some

cells

but

high

in

others

In

cells

in

which

the

concentration

is

high

X

activates

its

own

production

and

keeps

it

high

indefinitely

Strong

PAR

can

therefore

lead

to

a

differentiation-like

partitioning

of

cells

into

two

-LRB-

FIG.

-RRB-

In

some

cases

PAR

can

be

useful

as

a

memory

to

maintain

gene

expression

as

mentioned

below

-LRB-

see

the

section

on

developmental

a

networks

-RRB-

In

other

cases

a

bimodal

distribution

is

thought

to

help

cell

populations

to

maintain

a

mixed

phenotype

so

that

they

can

better

respond

to

a

stochastic

environment

-LRB-

reviewed

in

REF

-RRB-

Coherent

FFL

Coherent

type

X

Y

Z

Incoherent

FFL

Incoherent

type

X

Y

Z

Coherent

type

X

Y

Z

Incoherent

type

X

Y

Z

Coherent

type

X

Y

Z

Incoherent

type

X

Y

Z

Coherent

type

X

Y

Z

Incoherent

type

X

Y

Z

SX

X

SY

Y

Feedforward

loops

The

second

family

of

network

motifs

is

the

feedforward

loop

-LRB-

FFL

-RRB-

It

appears

in

hundreds

of

gene

systems

in

E.

coli

and

as

well

as

in

other

This

motif

consists

of

three

genes

a

regulator

X

which

regulates

Y

and

gene

Z

which

is

regulated

by

both

X

and

Y.

Because

each

of

the

three

regulatory

interactions

in

the

FFL

can

be

either

activation

or

repression

there

are

eight

possible

structural

types

of

FFL

-LRB-

FIG.

-RRB-

To

understand

the

function

of

the

FFLs

we

need

to

understand

how

X

and

Y

are

integrated

to

regulate

the

Z

Two

common

input

functions

are

an

AND

gate

in

which

both

X

and

Y

are

needed

to

activate

Z

and

an

OR

gate

in

which

binding

of

either

regulator

is

sufficient

Other

input

functions

are

possible

such

as

the

additive

input

function

in

the

flagella

and

the

hybrid

of

AND

and

OR

logic

in

the

lac

However

much

of

the

essential

behaviour

of

FFLs

can

be

understood

by

focusing

on

the

stereotypical

AND

and

OR

gates

Each

of

the

eight

FFL

types

can

thus

appear

with

at

least

two

input

functions

In

the

best

studied

transcriptional

networks

-LRB-

E.

coli

and

yeast

-RRB-

two

of

the

eight

FFL

types

occur

much

more

frequently

than

the

other

six

types

These

common

types

are

the

coherent

FFL

-LRB-

-RRB-

and

the

incoherent

FFL

-LRB-

-RRB-

Here

I

discuss

their

dynamical

functions

in

detail

the

functions

of

all

eight

FFL

types

are

described

in

REF

The

is

a

sign-sensitive

delay

element

and

a

persistence

detector

In

the

both

X

and

Y

are

transcriptional

activators

-LRB-

FIG.

-RRB-

I

will

first

consider

the

behaviour

of

the

FFL

when

the

Z

promoter

has

an

AND

input

function

and

then

turn

to

the

case

of

the

OR

input

function

With

an

AND

input

function

the

shows

a

delay

after

stimulation

but

no

delay

when

stimulation

stops

To

see

this

let

's

follow

the

behaviour

of

the

FFL

When

the

signal

Sx

appears

X

becomes

active

and

rapidly

binds

its

downstream

promoters

As

a

result

Y

begins

to

accumulate

However

owing

to

the

AND

input

function

Z

production

starts

only

when

Y

concentration

crosses

the

activation

threshold

for

the

Z

promoter

This

results

in

a

delay

of

Z

expression

following

the

appearance

of

Sx

-LRB-

FIG.

-RRB-

In

contrast

when

the

signal

Sx

is

removed

X

rapidly

becomes

inactive

As

a

result

Z

production

stops

because

deactivation

of

its

promoter

requires

only

one

arm

of

the

AND

gate

to

be

shut

off

Hence

there

is

no

delay

in

deactivation

of

Z

after

the

signal

Sx

is

removed

-LRB-

FIG.

-RRB-

This

dynamic

behaviour

is

called

sign-sensitive

delay

that

is

delay

depends

on

the

sign

of

the

Sx

step

An

ON

step

-LRB-

addition

of

Sx

-RRB-

causes

a

delay

in

Z

expression

but

an

OFF

step

-LRB-

removal

of

Sx

-RRB-

causes

no

delay

The

duration

of

the

delay

is

determined

by

the

biochemical

parameters

of

the

regulator

Y

for

example

the

b

X

SX

SY

Y

c

AND

Z

AND

Z

Figure

Feedforward

loops

-LRB-

FFLs

-RRB-

a

The

eight

types

of

feedforward

loops

-LRB-

FFLs

-RRB-

are

shown

In

coherent

FFLs

the

sign

of

the

direct

path

from

transcription

factor

X

to

output

Z

is

the

same

as

the

overall

sign

of

the

indirect

path

through

transcription

factor

Y.

Incoherent

FFLs

have

opposite

signs

for

the

two

paths

b

The

coherent

FFL

with

an

AND

input

function

at

the

Z

promoter

c

The

incoherent

FFL

with

an

AND

input

function

at

the

Z

promoter

SX

and

SY

are

input

signals

for

X

and

Y.

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a

Input

Sx

SX

X

SX

X

Y

Y

Y

AND

Z

Z

Delay

Z

Time

b

Arabinose

system

SX

cAMP

CRP

SY

arabinose

AraC

Lac

system

ON

step

of

SX

OFF

step

of

SX

araBAD

lacZYA

SX

cAMP

CRP

SY

allolactose

Z/Zst

Z/Zst

LacI

AND

Z

araBAD

araFGH

AND

Z

lacZYA

Time

-LRB-

min

-RRB-

Time

-LRB-

min

-RRB-

OFF

step

of

SX

c

ON

step

of

SX

SX

FlhDC

SY

Z/Zst

FliA

FliA

present

FliA

deleted

Z/Zst

60 80 100 120

60 80 100 120

OR

fliLMNOPQR

Time

-LRB-

min

-RRB-

Time

-LRB-

min

-RRB-

Figure

The

coherent

feedforward

loop

-LRB-

-RRB-

and

its

dynamics

a

The

with

an

AND

input

function

shows

delay

after

stimulus

-LRB-

SX

-RRB-

addition

and

no

delay

after

stimulus

removal

It

thus

acts

as

a

sign-sensitive

filter

which

responds

only

to

persistent

stimuli

b

An

experimental

study

of

the

in

the

arabinose

system

of

Escherichia

coli

using

fluorescent-reporter

strains

and

high-resolution

measurements

in

living

cells

This

system

-LRB-

represented

by

red

circles

-RRB-

shows

a

delay

after

addition

of

the

input

signal

-LRB-

cAMP

-RRB-

and

no

delay

after

its

removal

relative

to

a

simple-regulation

system

that

responds

to

the

same

input

signal

-LRB-

the

lac

system

represented

by

blue

squares

-RRB-

c

The

with

an

OR-like

input

function

in

the

flagella

system

of

E.

coli

shows

a

delay

after

signal

removal

but

not

after

the

onset

of

signal

-LRB-

represented

by

orange

circles

-RRB-

Deletion

of

the

Y

gene

-LRB-

FliA

-RRB-

abolishes

this

delay

-LRB-

represented

by

purple

squares

-RRB-

Z/Zst

Z

concentration

relative

to

the

steady

state

Zst

NATURE

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GENETICS

Nature

Publishing

Group

VOLUME

JUNE

REVIEWS

a

SX

Time

b

Simple

regulation

-LRB-

Simple

regulation

-RRB-

Time

c

cAMP

CRP

galactose

GalS

Z/Zst

galE-WT

higher

the

activation

threshold

for

the

Z

promoter

by

Y

the

longer

the

delay

The

delay

that

is

generated

by

the

FFL

can

be

useful

to

filter

out

brief

spurious

pulses

of

signal

A

signal

that

appears

only

briefly

does

not

allow

Y

to

accumulate

and

cross

its

threshold

and

thus

does

not

induce

a

Z

response

Only

persistent

signals

lead

to

Z

expression

-LRB-

FIG.

-RRB-

The

sign-sensitive

delay

function

of

this

motif

has

been

experimentally

demonstrated

in

the

arabinoseutilization

system

of

E.

coli

-LRB-

FIG.

-RRB-

A

delay

occurs

after

addition

of

the

input

signal

cAMP

but

not

after

its

removal

This

delay

of

about

min

is

on

the

same

timescale

as

spurious

pulses

of

cAMP

that

occur

in

the

natural

environment

when

E.

coli

transits

between

growth

conditions

When

the

Z

promoter

has

OR

logic

the

FFL

has

the

opposite

effect

to

the

AND

case

we

have

just

discussed

with

an

OR

input

function

the

shows

no

delay

after

stimulation

but

does

show

a

delay

when

stimulation

stops

To

see

this

note

that

when

the

signal

Sx

appears

X

alone

is

sufficient

to

activate

Z

because

of

the

OR-gate

logic

If

the

signal

suddenly

stops

after

a

long

period

of

stimulation

X

is

no

longer

active

but

the

presence

of

Y

is

still

enough

to

allow

production

of

Z.

Thus

the

with

OR

logic

allows

continued

production

in

the

face

of

a

transient

loss

of

the

input

signal

This

behaviour

was

experimentally

demonstrated

in

the

flagella

system

of

E.

coli

-LRB-

FIG.

-RRB-

The

flagella

motor

genes

are

regulated

in

an

FFL

that

has

input

functions

that

resemble

OR

gates

-LRB-

additive

functions

of

the

two

activators

FlhDC

and

FliA

-RRB-

The

flagella

FFL

was

found

to

prolong

flagella

gene

expression

after

the

input

signal

-LRB-

active

FlhDC

-RRB-

stopped

but

no

delay

occurred

when

the

input

signal

appeared

Mutations

and

conditions

that

inactivate

the

FliA

gene

in

this

FFL

lead

to

a

loss

of

this

delay

resulting

in

immediate

shut-off

of

the

flagella

genes

once

the

input

signal

stops

The

delay

in

the

flagella

system

of

about

hour

is

comparable

to

the

time

that

is

needed

for

the

biogenesis

of

a

complete

flagella

motor

The

is

a

pulse

generator

and

response

accelerator

In

the

the

two

arms

of

the

FFL

act

in

opposition

X

activates

Z

but

also

represses

Z

by

activating

the

repressor

Y

-LRB-

FIG.

-RRB-

As

a

result

when

a

signal

causes

X

to

assume

its

active

conformation

Z

is

rapidly

produced

-LRB-

FIG.

-RRB-

However

after

some

time

Y

levels

accumulate

to

reach

the

repression

threshold

for

the

Z

promoter

As

a

result

Z

production

decreases

and

its

concentration

drops

resulting

in

pulse-like

dynamics

-LRB-

FIG.

-RRB-

In

the

extreme

case

that

Y

completely

represses

Z

the

pulse

drops

to

zero

Pulse-like

dynamics

were

experimentally

demonstrated

in

a

synthetic

that

was

built

of

well-characterized

bacterial

regulators

in

E.

coli

In

this

FFL

the

activator

LuxR

-LRB-

X

-RRB-

was

made

to

activate

both

a

GFP

reporter

-LRB-

Z

-RRB-

and

the

repressor

-LRB-

Y

-RRB-

which

repressed

the

Z

promoter

In

addition

to

pulse-like

dynamics

the

can

carry

out

another

dynamical

function

response

acceleration

In

cases

in

which

Y

does

not

completely

repress

Z/Zst

Z

Y

galE-mut

galETK

Time

-LRB-

cell

generations

-RRB-

Figure

The

incoherent

feeforward

loop

-LRB-

-RRB-

and

its

dynamics

a

The

can

generate

a

pulse

of

Z

expression

in

response

to

a

step

stimulus

of

Sx

This

occurs

because

once

Y

has

passed

its

threshold

-LRB-

indicated

by

an

orange

circle

-RRB-

it

starts

to

repress

Z.

b

The

shows

faster

response

time

for

the

concentration

of

protein

Z

than

a

simple-regulation

circuit

with

the

same

steadystate

expression

level

c

An

experimental

study

of

the

dynamics

of

the

in

the

galactose

system

of

E.

coli

Response

acceleration

in

the

wild-type

system

-LRB-

marked

galE-WT

-RRB-

is

found

following

steps

of

the

input

signal

-LRB-

glucose

starvation

-RRB-

The

acceleration

is

disrupted

when

the

effect

of

the

repressor

GalS

is

abolished

by

mutating

its

binding

site

in

the

promoter

of

the

output

gene

operon

galETK

-LRB-

marked

galE-mut

-RRB-

response

time

Z/Zst

Z

concentration

relative

to

the

steady

state

JUNE

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Nature

Publishing

Group

REVIEWS

a

X

X

b

X

activity

X

threshold

threshold

threshold

Zn

Z

activity

argR

argCBH

argD

argE

argF

argI

Time

Figure

The

single-input

module

-LRB-

SIM

-RRB-

network

motif

and

its

dynamics

a

The

single-input

module

-LRB-

SIM

-RRB-

network

motif

and

an

example

from

the

arginine-biosynthesis

system

b

Temporal

order

of

expression

in

a

SIM

As

the

activity

of

the

master

regulator

X

changes

in

time

it

crosses

the

different

activation

threshold

of

the

genes

in

the

SIM

at

different

times

generating

a

temporal

order

of

expression

the

production

of

Z

Z

concentration

reaches

a

certain

non-zero

steady-state

level

Because

of

the

strong

initial

production

of

Z

in

the

time

period

before

Y

represses

the

Z

promoter

Z

reaches

its

steady-state

rapidly

The

response

time

is

shorter

than

that

of

a

corresponding

simple-regulation

system

-LRB-

FIG.

-RRB-

Note

that

although

both

NAR

and

can

speed

up

responses

NAR

works

only

on

transcription

factors

-LRB-

or

genes

that

lie

on

the

same

operon

with

transcription

factors

-RRB-

whereas

the

can

accelerate

any

target

gene

Z

Such

response

acceleration

was

observed

experimentally

in

the

galactose

utilization

system

of

E.

coli

-LRB-

FIG.

-RRB-

Here

onset

of

glucose

starvation

in

the

absence

of

galactose

leads

to

a

rapid

induction

of

the

galactose-utilization

genes

to

a

moderate

level

of

expression

The

response

time

of

this

system

is

about

threefold

faster

than

that

of

a

simple-regulation

system

that

responds

to

the

same

signal

-LRB-

the

lac

system

-RRB-

This

speed-up

was

dependent

on

the

in

mutants

and

conditions

in

which

the

motif

was

disrupted

speed-up

was

abolished

and

the

dynamics

resembled

simple

regulation

-LRB-

FIG.

-RRB-

Note

that

network

motifs

can

utilize

not

only

transcription

factor

proteins

but

also

microRNAs

-LRB-

miRNAs

-RRB-

For

example

an

in

mammalian

cells

involves

MYC

as

activator

X

as

the

target

gene

Z

and

a

miRNA

in

the

role

of

the

repressor

Diverse

FFL

motifs

with

miRNAs

have

been

found

in

Caenorhabditis

The

NAR

and

PAR

network

motifs

are

sometimes

integrated

into

FFLs

usually

on

the

regulator

Y

These

regulatory

loops

can

help

to

speed

up

or

slow

down

the

response

time

of

Y

enhancing

the

behaviour

of

the

FFLs

The

dynamical

functions

of

FFLs

can

be

tuned

by

varying

the

molecular

parameters

of

the

circuit

Changes

in

parameters

such

as

the

production

rates

or

the

activation

thresholds

of

the

regulators

can

as

mentioned

above

determine

the

delay

in

the

or

the

acceleration

factor

of

the

This

tuning

can

be

captured

by

simple

Similar

functions

can

in

principle

be

accomplished

by

other

circuits

that

resemble

FFLs

but

with

longer

branches

that

diverge

and

then

merge

back

However

such

larger

circuits

are

rarely

found

in

known

transcription

networks

The

FFL

can

potentially

perform

additional

computational

functions

as

suggested

by

theoretical

Multi-output

FFLs

The

FFLs

in

transcription

networks

tend

to

combine

to

form

multi-output

in

which

X

and

Y

regulate

multiple

output

genes

Zn

In

these

configurations

each

of

the

output

genes

benefits

from

the

dynamical

functions

that

are

described

above

In

addition

the

multi-output

FFL

can

generate

temporal

orders

of

gene

activation

and

inactivation

by

means

of

a

hierarchy

of

regulation

thresholds

for

the

different

promoters

This

was

experimentally

demonstrated

using

the

flagella

mutations

in

the

promoter

regions

that

changed

the

activation

thresholds

were

able

to

reprogramme

the

temporal

order

of

the

Further

experimental

tests

of

the

dynamical

behaviour

of

FFLs

in

living

cells

would

be

of

great

interest

especially

in

organisms

other

than

E.

coli

Single-input

modules

-LRB-

SIM

-RRB-

Our

third

family

of

network

motifs

have

a

simple

pattern

in

which

a

regulator

X

regulates

a

group

of

target

genes

-LRB-

FIG.

-RRB-

In

the

purest

form

no

other

regulator

regulates

any

of

these

genes

hence

the

name

single-input

module

X

also

typically

regulates

itself

The

main

function

of

this

motif

is

to

allow

coordinated

expression

of

a

group

of

genes

with

shared

function

In

addition

this

motif

has

a

more

subtle

dynamical

property

that

is

similar

to

that

of

the

multi-output

FFLs

that

are

discussed

above

it

can

generate

a

temporal

expression

programme

with

a

defined

order

of

activation

of

each

of

the

target

promoters

X

often

has

different

activation

thresholds

for

each

gene

owing

to

variations

in

the

sequence

and

context

of

its

binding

site

in

each

promoter

So

when

X

activity

rises

gradually

with

time

it

crosses

these

thresholds

in

a

defined

order

first

the

lowest

threshold

then

the

next

lowest

threshold

an

so

on

resulting

in

a

temporal

order

of

expression

-LRB-

FIG.

-RRB-

Similar

reasoning

applies

when

X

acts

as

a

repressor

VOLUME

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Publishing

Group

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Xn

also

be

specified

Currently

most

of

the

input

functions

in

any

organism

are

Once

these

functions

are

characterized

for

example

by

high-resolution

mapping

using

fluorescent-reporter

it

will

be

interesting

to

study

the

detailed

function

of

DORs

rpoS

ada

oxyR

ihf

lrp

hns

rcsA

Zm

nhaR

crp

fis

alkA

katG

dps

osmC

ftsQAZ

nhaA

proP

Figure

The

dense

overlapping

regulon

-LRB-

DOR

-RRB-

network

motif

In

this

motif

many

inputs

regulate

many

outputs

-LRB-

top

panel

-RRB-

The

bottom

panel

shows

an

example

from

the

stress-response

system

of

Escherichia

coli

Such

a

temporal

order

has

been

observed

experimentally

in

several

E.

coli

systems

with

SIM

architecture

that

have

been

studied

at

high

temporal

Importantly

the

temporal

order

seems

to

match

the

functional

order

of

the

genes

The

earlier

a

gene

is

needed

in

a

multi-gene

process

the

earlier

its

promoter

is

activated

This

kind

of

programme

can

prevent

protein

production

before

it

is

needed

For

example

the

arginine-biosynthesis

system

shows

a

SIM

design

in

which

the

repressor

ArgR

regulates

several

operons

that

encode

enzymes

in

the

arginine-biosynthesis

pathway

When

arginine

is

removed

from

the

medium

these

promoters

are

activated

in

a

temporal

order

with

minutes

between

promoter

The

order

of

activation

matches

the

position

of

the

enzymes

in

the

arginine-biosynthesis

pathway

The

same

principle

applies

to

other

linear

biosynthesis

and

stress-response

systems

such

as

the

SOS

DNA

repair

Many

other

examples

of

temporal

order

are

known

including

the

flagella

systems

of

E.

and

Caulobacter

cell-cycle

gene

systems

in

many

and

developmental

The

global

organization

of

network

motifs

The

four

motif

families

that

have

been

discussed

seem

to

cover

most

of

the

known

interactions

in

the

transcription

networks

of

E.

coli

and

yeast

As

such

they

appear

to

be

the

main

building

blocks

of

these

sensory

networks

How

do

these

network

motifs

combine

to

form

the

global

structure

of

the

networks

To

answer

this

question

an

image

of

the

network

is

required

Network

motifs

can

help

to

portray

the

network

in

a

compact

way

by

using

symbols

to

denote

SIMs

DORs

and

FFLs

-LRB-

see

REF

for

an

example

-RRB-

This

kind

of

arrangement

shows

that

FFLs

and

SIMs

are

integrated

into

the

DORs

The

DORs

occur

in

a

single

layer

there

is

no

DOR

at

the

output

of

a

second

DOR

Thus

most

computations

are

carried

out

in

a

single

cortex

of

promoters

at

the

DOR

output

Furthermore

long

regulatory

cascades

are

most

genes

are

regulated

just

one

step

away

from

their

activator

-LRB-

with

relatively

few

exceptions

-RRB-

One

possible

reason

for

this

shallow

architecture

is

the

need

for

rapid

response

as

mentioned

above

it

can

take

up

to

one

cell-generation

time

to

pass

a

signal

down

each

step

of

a

cascade

Sensory

networks

that

are

designed

to

respond

rapidly

to

external

signals

might

therefore

be

limited

in

their

use

of

long

A

view

of

network-motif

behaviour

within

the

global

dynamics

of

gene

networks

can

be

gleaned

by

means

of

DNA

For

example

transcription

dynamics

of

mammalian

genes

in

response

to

growthfactor

stimulation

could

be

related

to

network

Future

work

in

this

direction

could

help

us

to

refine

our

understanding

of

motif

dynamics

and

study

the

interactions

between

network

motifs

Network

motifs

in

developmental

networks

So

far

I

have

discussed

sensory

transcription

networks

that

respond

rapidly

and

make

reversible

decisions

Let

us

now

turn

to

developmental

transcription

networks

that

transduce

signals

into

cell-fate

These

networks

have

different

constraints

they

usually

function

on

the

timescale

of

one

or

several

cell

generations

and

often

need

to

make

irreversible

decisions

that

last

even

after

the

input

signal

has

vanished

Developmental

transcription

networks

use

all

the

network

motifs

described

above

In

addition

as

a

result

of

their

specific

requirements

developmental

networks

use

several

other

network

motifs

that

are

not

commonly

found

in

sensory

networks

Feedback

loops

comprising

two

transcription

interactions

Developmental

transcription

networks

often

use

positive-feedback

loops

that

are

made

up

of

two

transcription

factors

that

regulate

each

other

There

are

two

kinds

of

positive-feedback

loops

a

doublepositive

loop

and

a

double-negative

loop

-LRB-

FIG.

-RRB-

The

double-positive

loop

in

which

two

activators

activate

Dense

overlapping

regulons

-LRB-

DOR

-RRB-

The

final

family

of

network

motifs

that

are

present

in

sensory

transcription

networks

consist

of

a

set

of

regulators

that

combinatorially

control

a

set

of

output

-LRB-

FIG.

-RRB-

These

motifs

are

referred

to

as

dense

overlapping

regulons

-LRB-

DORs

-RRB-

or

multi-input

motifs

-LRB-

MIMs

-RRB-

E.

coli

has

several

DORs

with

hundreds

of

output

genes

each

responsible

for

a

broad

biological

function

such

as

carbon

utilization

anaerobic

growth

stress

response

and

so

on

Similar

patterns

are

found

in

The

DOR

can

be

thought

of

as

a

gate-array

carrying

out

a

computation

by

which

multiple

inputs

are

translated

into

multiple

outputs

So

to

fully

understand

the

function

of

the

DOR

the

connectivity

arrows

are

not

the

input

functions

in

the

promoter

of

each

output

gene

must

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a

Z

b

Z

X

Y

X

Z

activity

Y

Z

activity

Memory

Memory

X

Memory

X

Y

Y

Memory

Time

Time

c

AND

AND

AND

AND

Z

Time

Figure

Network

motifs

in

developmental

transcription

networks

a

Network

motifs

with

a

double-positivefeedback

loop

When

Z

is

activated

proteins

X

and

Y

begin

to

be

produced

They

can

remain

locked

ON

even

when

Z

is

deactivated

-LRB-

at

times

after

the

dashed

line

-RRB-

b

Regulated

feedback

with

a

double-negative-feedback

loop

Here

Z

acts

to

switch

the

steady

states

Initially

Y

concentration

is

high

and

represses

X

expression

After

Z

is

activated

X

is

produced

and

Y

is

repressed

This

state

can

persist

even

after

Z

is

deactivated

Thus

the

feedback

implements

a

memory

c

A

transcription

network

that

guides

the

development

of

the

Bacillus

subtilis

and

represent

groups

of

tens

to

hundreds

of

genes

This

network

is

made

of

two

incoherent

feedforward

loops

-LRB-

-RRB-

which

generate

pulses

of

and

and

two

coherent

feedforward

loops

-LRB-

FFLs

-RRB-

one

of

which

generates

a

delayed

step

each

other

has

two

steady

states

either

both

X

and

Y

are

OFF

or

both

are

ON

The

double-negative

loop

in

which

two

repressors

repress

each

other

has

different

steady

states

either

X

is

ON

and

Y

is

OFF

or

the

opposite

In

both

cases

a

transient

signal

can

cause

the

loop

to

lock

irreversibly

into

a

steady

state

In

this

sense

this

network

motif

can

provide

memory

of

an

input

signal

even

after

the

input

signal

is

gone

Often

X

and

Y

also

positively

regulate

themselves

strengthening

the

memory

effects

The

same

motif

can

also

comprise

or

post-transcriptional

interactions

such

as

Positive-feedback

loops

can

regulate

or

be

regulated

by

other

In

a

regulating

loop

two

regulators

X

and

Y

form

a

feedback

loop

and

also

jointly

regulate

downstream

Z

genes

A

double-positive

loop

between

X

and

Y

is

useful

for

decisions

whereby

the

cell

irreversibly

assumes

a

fate

in

response

to

a

transient

developmental

signal

Genes

that

are

specific

to

the

cell

fate

can

be

coactivated

by

X

and

Y

A

double-negative

loop

-LRB-

FIG.

-RRB-

is

useful

in

this

motif

as

a

toggle

switch

between

two

different

such

as

lysogeny

and

lysis

in

The

genes

that

are

activated

by

X

are

repressed

by

Y

and

the

opposite

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Box

Network

motifs

in

other

biological

networks

Composite

negative-feedback

loops

In

addition

to

transcription

networks

one

can

seek

composite

network

motifs

that

include

different

types

of

X

X

One

of

the

most

common

composite

motifs

is

a

negative-feedback

loop

between

two

proteins

in

which

one

arm

is

a

transcriptional

interaction

-LRB-

solid

arrow

-RRB-

and

the

other

arm

is

a

protein

protein

interaction

-LRB-

broken

arrow

-RRB-

An

example

is

the

and

loop

Y

Y

involved

in

monitoring

stresses

and

DNA

damage

in

human

cells

Composite

negative-feedback

loops

seem

to

be

much

more

common

than

purely

transcriptional

Diamond

Multi-layer

perceptrons

negative-feedback

loops

The

separation

of

timescales

between

the

slow

transcription

arm

and

the

faster

protein-interaction

arm

might

help

to

stabilize

the

dynamics

of

composite

loops

avoiding

feedback

at

a

delay

that

promotes

instability

-LRB-

as

observed

in

a

synthetic

three-repressor

-RRB-

Experiments

on

individual

living

cells

have

shown

that

negative-feedback

loops

embedded

within

additional

interactions

can

sometimes

generate

oscillations

whereby

the

levels

of

X

and

Y

rise

and

Oscillations

in

biological

systems

are

often

generated

by

a

composite

negative-feedback

loop

coupled

to

a

second

positive-feedback

-LRB-

right

hand

side

of

the

top

panel

-RRB-

The

same

motif

with

different

parameters

can

also

lead

to

stochastic

excitable

systems

that

occasionally

generate

a

single

large

output

Networks

of

protein

modification

notably

signal-transduction

networks

also

seem

to

display

network

Here

nodes

-LRB-

orange

circles

-RRB-

are

signalling

proteins

and

edges

-LRB-

arrows

-RRB-

represent

modifications

such

as

phosphorylation

Signal-transduction

networks

show

feedforward

loops

-LRB-

FFLs

-RRB-

as

well

as

motifs

that

are

not

present

in

transcription

networks

such

as

the

diamond

pattern

Diamonds

combine

to

form

multi-layer

perceptron

motifs

that

are

composed

of

three

or

more

layers

of

signalling

Such

patterns

can

potentially

carry

out

elaborate

functions

on

multiple

input

signals

including

generalization

of

information

from

partial

They

also

can

show

graceful

degradation

of

performance

upon

loss

of

Current

high-quality

data

on

protein

protein

interactions

is

more

limited

than

data

on

transcription

interactions

It

is

likely

that

additional

motifs

will

be

discovered

once

data

on

protein

interaction

networks

becomes

more

complete

Differences

in

timescale

spatial

organization

and

precision

between

signalling

processes

and

transcription

processes

are

likely

to

underlie

the

differences

in

the

network

motifs

that

are

found

in

these

networks

Networks

of

synaptic

connection

between

neurons

also

seem

to

exhibit

network

motifs

Cortical

circuitry

harbours

triplets

of

neurons

that

are

connected

as

In

particular

many

neuron

types

are

wired

as

incoherent

feedforward

loops

-LRB-

-RRB-

with

afferent

input

as

X

an

inhibitory

neuron

from

which

outputs

are

restricted

to

a

specific

brain

region

as

Y

and

a

relay

neuron

that

sends

connection

to

other

regions

as

The

fully

mapped

synaptic

network

of

Caenorhabditis

shows

motifs

including

diamonds

and

multi-layered

as

well

as

two-neuron

feedback

Mammalian

neuronal

networks

also

display

significant

network

as

assayed

using

electrical

measurements

of

neuron

It

is

an

interesting

question

whether

neuronal

motifs

carry

out

similar

computational

functions

to

the

motifs

that

are

discussed

in

the

main

text

In

a

regulated

loop

two

regulators

X

and

Y

form

a

feedback

loop

and

are

both

regulated

by

an

upstream

regulator

Z

-LRB-

FIG.

b

-RRB-

In

this

motif

an

activator

Z

can

be

used

to

lock

a

double-positive

loop

into

an

ON

In

the

case

of

a

double-negative

loop

Z

can

activate

X

and

repress

Y

-LRB-

or

the

opposite

-RRB-

and

thus

act

to

switch

the

system

between

its

two

steady

states

Many

positivefeedback

loops

are

both

regulated

and

regulating

Variants

of

this

motif

include

cases

in

which

Z

inputs

to

only

one

of

the

two

regulators

or

each

regulator

has

its

own

independent

Transcription

cascade

In

addition

to

motifs

that

use

feedback

loops

developmental

transcription

networks

tend

to

have

much

longer

cascades

than

sensory

transcription

These

cascades

pass

information

on

a

slow

timescale

which

can

be

on

the

order

of

one

cell

generation

at

each

cascade

step

-LRB-

or

for

degradable

regulators

the

half-life

of

the

regulator

at

each

step

-RRB-

an

appropriate

pace

for

many

developmental

processes

Development

often

uses

repressor

cascades

the

timing

properties

of

which

can

often

be

more

robust

to

noise

in

protein-production

rates

than

those

of

activator

Cascades

are

also

commonly

found

in

signalling

networks

which

function

on

a

faster

timescale

than

transcription

networks

-LRB-

BOX

-RRB-

An

interlocked

FFL

circuit

in

development

The

FFLs

in

developmental

networks

often

combine

into

larger

and

more

complex

transcription

circuits

than

in

sensory

networks

Can

we

still

understand

the

dynamics

of

such

large

circuits

on

the

basis

of

the

behaviour

of

the

individual

FFLs

To

address

this

question

I

will

discuss

a

well

mapped

developmental

network

that

is

composed

of

interlocking

FFLs

This

circuit

governs

differentiation

in

the

bacterium

Bacillus

subtilis

When

starved

B.

subtilis

cells

differentiate

into

durable

spores

To

produce

a

spore

B.

subtilis

must

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make

many

proteins

that

are

not

found

in

the

growing

bacterium

This

process

termed

sporulation

involves

hundreds

of

genes

These

genes

are

turned

ON

and

OFF

in

a

series

of

temporal

waves

each

carrying

out

specific

stages

in

the

formation

of

the

spore

The

network

that

regulates

this

includes

several

transcription

factors

that

are

arranged

in

linked

and

-LRB-

FIG.

-RRB-

To

initiate

the

sporulation

process

a

starvation

signal

Sx

activates

-LRB-

an

activator

called

-RRB-

acts

in

an

together

with

to

control

the

genes

This

generates

a

pulse

of

expression

A

with

AND

logic

is

formed

by

and

that

is

both

are

required

to

activate

This

ensures

that

is

not

activated

unless

the

Sx

signal

is

persistent

Next

acts

in

an

by

which

it

generates

a

pulse

of

genes

timed

at

a

delay

relative

to

the

first

pulse

Finally

and

together

join

in

an

AND-gate

to

activate

genes

which

are

turned

on

last

The

result

is

a

three-wave

temporal

pattern

first

a

pulse

of

expression

followed

by

a

pulse

of

expression

followed

by

expression

of

the

late

genes

-LRB-

FIG.

-RRB-

The

FFLs

in

this

network

are

combined

in

a

way

that

utilizes

their

delay

and

pulse-generating

features

to

generate

a

temporal

programme

of

gene

expression

The

FFLs

that

control

and

are

actually

multi-output

FFLs

because

and

each

represent

groups

of

genes

This

design

can

generate

finer

temporal

programmes

within

each

group

of

genes

The

FFLs

in

this

network

therefore

seem

to

be

linked

in

a

way

that

allows

easy

interpretation

on

the

basis

of

the

dynamics

of

each

FFL

in

isolation

It

is

interesting

to

consider

whether

such

modular

design

applies

to

network

motifs

in

other

systems

a

question

that

can

be

addressed

experimentally

only

a

few

mutations

to

remove

the

binding

site

of

a

regulator

in

a

given

promoter

and

thereby

lose

an

arrow

in

a

Hence

even

closely

related

organisms

often

have

different

network

motifs

to

regulate

a

given

gene

provided

that

they

live

in

different

environments

as

was

demonstrated

by

Babu

et

al.

One

hypothesis

is

that

the

network

motifs

are

selected

according

to

the

computations

that

are

required

in

the

environment

of

each

species

For

example

the

selective

advantage

of

FFLs

in

different

environments

was

treated

theoretically

in

REF

Network

motifs

might

have

been

rediscovered

by

evolution

because

they

perform

important

functions

They

seem

to

be

the

most

and

use

the

least

number

of

components

of

the

large

set

of

circuits

that

can

carry

out

equivalent

functions

Intriguingly

network

motifs

are

also

found

in

various

other

biological

networks

-LRB-

BOX

-RRB-

Detection

of

network

motifs

Open-source

software

that

can

detect

network

motifs

is

-LRB-

see

the

Uri

Alon

laboratory

web

site

-RRB-

This

software

accepts

a

network

as

input

and

detects

network

motifs

as

patterns

that

occur

more

often

in

the

network

than

in

random

networks

with

the

same

size

and

connectivity

properties

The

software

accepts

network

data

in

the

form

of

a

list

that

details

the

interactions

that

occur

between

different

nodes

-LRB-

and

optionally

the

type

of

interaction

-RRB-

The

software

outputs

the

recurring

network

motifs

and

depicts

these

motifs

within

the

network

Future

directions

Experiments

in

living

cells

and

mathematical

modelling

have

helped

to

define

some

of

the

functions

of

network

motifs

Much

remains

to

be

studied

predictions

about

the

functions

of

motifs

must

be

tested

experimentally

in

the

different

systems

in

which

the

motifs

appear

As

most

experiments

so

far

have

used

bacteria

it

would

be

important

to

test

network

motifs

in

eukaryotic

organisms

In

the

examples

studied

so

far

there

was

a

good

agreement

between

the

theoretical

predictions

and

experimental

tests

The

specific

ways

in

which

the

network

motifs

in

these

examples

are

wired

together

have

allowed

us

to

understand

the

dynamics

of

each

individual

motif

even

when

it

is

connected

to

the

rest

of

the

networks

of

the

cell

As

more

systems

are

investigated

it

is

likely

that

more

complicated

cases

will

be

found

in

which

the

behaviour

of

motifs

is

affected

to

a

much

greater

extent

by

its

context

within

the

rest

of

the

network

this

is

an

open

field

for

research

As

networks

become

better

characterized

new

motifs

and

new

motif

functions

will

doubtless

be

discovered

Network

motifs

at

the

level

of

signalling

networks

and

neuronal

networks

are

only

beginning

to

be

investigated

If

the

current

findings

can

be

generalized

they

suggest

that

complex

biological

networks

have

a

degree

of

structural

simplicity

in

that

they

contain

a

limited

set

of

network

motifs

Experimental

testing

of

the

functions

of

each

motif

might

explain

why

these

motifs

have

been

selected

again

and

again

in

evolution

This

raises

the

hope

that

the

dynamics

of

large

networks

can

be

understood

in

terms

of

elementary

circuit

VOLUME

JUNE

Convergent

evolution

of

network

motifs

How

did

network

motifs

evolve

The

most

common

form

of

evolution

for

genes

is

conservative

evolution

whereby

two

genes

that

have

similar

functions

stem

from

a

common-ancestor

gene

This

is

reflected

in

a

significant

degree

of

sequence

similarity

between

the

genes

called

gene

homology

Did

network

motifs

such

as

FFLs

evolve

in

a

similar

way

in

that

an

ancestral

FFL

duplicated

and

gave

rise

to

the

present

FFLs

In

most

cases

it

seems

that

the

answer

is

no

For

example

homologous

genes

Z

and

Z

in

two

organisms

are

often

both

regulated

by

FFLs

in

response

to

similar

environmental

stimuli

If

the

two

FFLs

had

a

common-ancestor

FFL

the

regulators

X

and

Y

in

these

systems

would

also

be

homologous

However

this

is

generally

not

the

case

The

sequence

of

the

regulators

is

sometimes

so

different

that

they

are

classed

into

completely

different

transcription

factor

families

The

same

applies

to

SIM

and

DOR

network

motifs

similar

output

genes

in

different

organisms

are

often

regulated

by

unrelated

transcription

It

therefore

seems

that

in

many

cases

evolution

has

converged

independently

on

the

same

regulation

To

understand

convergent

network-motif

evolution

it

is

important

to

note

that

transcription

networks

seem

to

rewire

rapidly

on

evolutionary

it

takes

NATURE

REVIEWS

GENETICS

Nature

Publishing

Group

REVIEWS

Alon

U.

Introduction

to

Systems

Biology

Design

Principles

Of

Biological

Circuits

-LRB-

CRC

Boca

Raton

-RRB-

Davidson

E.

H

The

Regulatory

Genome

Gene

Regulatory

Networks

In

Development

And

Evolution

-LRB-

Academic

Burlington

-RRB-

Ptashne

M.

Gann

A.

Genes

Signals

-LRB-

Cold

Spring

Harbor

Laboratory

Press

Cold

Spring

Harbor

-RRB-

Levine

M.

Davidson

E.

H.

Gene

regulatory

networks

for

development

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Thieffry

D.

Huerta

A.

M.

Perez-Rueda

E.

Collado-Vides

J.

From

specific

gene

regulation

to

genomic

networks

a

global

analysis

of

transcriptional

regulation

in

Escherichia

coli

Bioessays

-LRB-

-RRB-

Shen-Orr

S.

S.

Milo

R.

Mangan

S.

Alon

U.

Network

motifs

in

the

transcriptional

regulation

network

of

Escherichia

coli

Nature

Genet

-LRB-

-RRB-

Milo

R.

et

al.

Network

motifs

simple

building

blocks

of

complex

networks

Science

-LRB-

-RRB-

Eichenberger

P.

et

al.

The

program

of

gene

transcription

for

a

single

differentiating

cell

type

during

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Acknowledgements

I

thank

my

laboratory

members

for

stimulating

discussions

especially

R.

Milo

S.

Itzkovitz

S.

Mangan

S.

Shen-Orr

and

N.

Kashtan

I

thank

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Elowitz

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Surette

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Leibler

and

H.

Westerhoff

for

discussions

and

the

Israel

Science

Foundation

the

Human

Frontiers

Science

Foundation

Minerva

National

Institutes

of

Health

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USA

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and

the

Kahn

Family

Foundation

for

support

Competing

interests

statement

The

author

declares

no

competing

financial

interests

DATABASES

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terms

in

this

article

are

linked

online

to

Entrez

Gene

fcgi?db

gene

FlhDC

FliA

UniProtKB

ArgR

LexA

MYC

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