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Integrated

analysis

of

regulatory

and

metabolic

networks

reveals

novel

regulatory

mechanisms

in

Saccharomyces

cerevisiae

Markus

J.

Herrg

rd

Baek-Seok

Lee

Vasiliy

Portnoy

and

Bernhard

Palsson

Genome

Res

originally

published

online

Apr

Access

the

most

recent

version

at

doi

gr

Supplementary

data

References

Supplemental

Research

Data

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Cold

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Cold

Spring

Harbor

Laboratory

Press

Letter

Integrated

analysis

of

regulatory

and

metabolic

networks

reveals

novel

regulatory

mechanisms

in

Saccharomyces

cerevisiae

Markus

J.

Herrg

rd

Baek-Seok

Lee

Vasiliy

Portnoy

and

Bernhard

Department

of

Bioengineering

University

of

California

San

Diego

La

Jolla

California

USA

We

describe

the

use

of

model-driven

analysis

of

multiple

data

types

relevant

to

transcriptional

regulation

of

metabolism

to

discover

novel

regulatory

mechanisms

in

Saccharomyces

cerevisiae

We

have

reconstructed

the

nutrient-controlled

transcriptional

regulatory

network

controlling

metabolism

in

S.

cerevisiae

consisting

of

transcription

factors

regulating

metabolic

genes

based

on

information

in

the

primary

literature

This

reconstructed

regulatory

network

coupled

with

an

existing

genome-scale

metabolic

network

model

allows

in

silico

prediction

of

growth

phenotypes

of

regulatory

gene

deletions

as

well

as

gene

expression

profiles

We

compared

model

predictions

of

gene

expression

changes

in

response

to

genetic

and

environmental

perturbations

to

experimental

data

to

identify

potential

novel

targets

for

transcription

factors

We

then

identified

regulatory

cascades

connecting

transcription

factors

to

the

potential

targets

through

a

systematic

model

expansion

strategy

using

published

genome-wide

chromatin

immunoprecipitation

and

binding-site-motif

data

sets

Finally

we

show

the

ability

of

an

integrated

metabolic

and

regulatory

network

model

to

predict

growth

phenotypes

of

transcription

factor

knockout

strains

These

studies

illustrate

the

potential

of

model-driven

data

integration

to

systematically

discover

novel

components

and

interactions

in

regulatory

and

metabolic

networks

in

eukaryotic

cells

-LSB-

Supplemental

material

is

available

online

at

-RSB-

Current

biological

research

produces

increasing

volumes

of

data

describing

cellular

components

their

interactions

and

states

of

biological

networks

for

model

organisms

including

the

budding

yeast

Saccharomyces

cerevisiae

Component

and

interaction

data

including

genome

sequences

-LRB-

Bussey

et

al.

-RRB-

protein

complexes

-LRB-

Gavin

et

al.

-RRB-

and

protein

DNA

interactions

-LRB-

Harbison

et

al.

-RRB-

can

be

used

to

establish

the

connectivity

of

the

biochemical

networks

inside

the

cell

System-state

data

types

including

gene

expression

-LRB-

DeRisi

et

al.

-RRB-

metabolite

level

-LRB-

Villas-Boas

et

al.

-RRB-

metabolic

flux

-LRB-

Sauer

-RRB-

and

highthroughput

deletion

strain

phenotyping

-LRB-

Giaever

et

al.

-RRB-

data

represent

the

states

and

outputs

of

these

networks

Connecting

large-scale

component

and

interaction

information

to

data

on

system

states

in

order

to

facilitate

the

interpretation

of

both

data

types

is

a

major

challenge

in

systems

biology

The

data

integration

and

interpretation

task

is

made

challenging

by

the

incompleteness

and

noisiness

of

large-scale

data

sets

-LRB-

Grunenfelder

and

Winzeler

-RRB-

Given

these

issues

with

large-scale

data

sets

systematic

inclusion

of

literature-derived

information

on

network

structures

into

the

analysis

represents

an

appealing

alternative

to

purely

data-driven

approaches

The

widespread

availability

of

component

and

biochemical

interaction

information

in

the

primary

literature

has

enabled

the

reconstruction

of

chemically

and

biologically

consistent

mathematical

descriptions

of

biochemical

networks

in

well-studied

model

organisms

-LRB-

Herrg

rd

et

al.

Price

et

al.

-RRB-

These

network

models

can

then

be

used

to

Present

address

Institute

for

Advanced

Biosciences

Keio

University

Tsuruoka

Yamagata

Japan

Corresponding

author

E-mail

palsson@ucsd.edu

fax

-LRB-858-RRB- 822-3120

Article

published

online

before

print

Article

and

publication

date

are

at

http

predict

changes

in

system

states

in

response

to

genetic

and

environmental

perturbations

Furthermore

model

predictions

can

be

directly

compared

with

experimental

data

obtained

for

example

by

metabolic

flux

or

gene

expression

profiling

-LRB-

Covert

et

al.

Price

et

al.

-RRB-

As

a

result

of

these

comparisons

modifications

to

the

biochemical

network

model

that

would

improve

its

ability

to

predict

system

states

can

be

identified

to

iteratively

improve

the

model

In

the

case

of

metabolic

networks

the

network

reconstruction

step

can

now

be

routinely

done

and

has

been

accomplished

for

a

number

of

key

model

organisms

including

Escherichia

coli

-LRB-

Reed

et

al.

-RRB-

and

Saccharomyces

cerevisiae

-LRB-

Famili

et

al.

Duarte

et

al.

-RRB-

Using

a

reconstructed

genome-scale

stoichiometric

matrix

as

a

starting

point

the

constraint-based

modeling

framework

-LRB-

Price

et

al.

-RRB-

can

then

be

used

to

make

phenotypic

predictions

that

can

be

compared

to

experimental

data

Frequently

used

constraint-based

approaches

include

fluxbalance

analysis

-LRB-

FBA

-RRB-

that

allows

the

prediction

of

flux

distributions

and

growth

rates

based

on

known

metabolic

network

stoichiometry

growth

media

composition

and

biomass

composition

-LRB-

Kauffman

et

al.

-RRB-

The

constraint-based

framework

has

been

extended

to

account

for

transcriptional

regulation

of

metabolism

in

the

form

of

the

regulated

flux-balance

analysis

-LRB-

rFBA

-RRB-

approach

-LRB-

Covert

et

al.

-RRB-

This

approach

requires

first

reconstructing

the

transcriptional

regulatory

network

structure

in

the

form

of

Boolean

rules

based

on

the

various

available

data

sources

and

then

integrating

this

network

with

the

metabolic

network

model

So

far

the

rFBA

approach

has

been

applied

to

make

predictions

of

growth

phenotypes

and

qualitative

gene

expression

changes

in

E.

coli

-LRB-

Covert

and

Palsson

Covert

et

al.

-RRB-

Here

we

present

the

first

large-scale

integrated

model

of

metabolism

and

its

transcriptional

regulation

in

a

eukaryotic

organ

by

Cold

Spring

Harbor

Laboratory

Press

ISSN

Genome

Research

Downloaded

from

on

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Published

by

Cold

Spring

Harbor

Laboratory

Press

Herrg

rd

et

al.

ism

S.

cerevisiae

The

current

model

includes

the

majority

of

known

nutrient-controlled

transcriptional

regulation

-LRB-

transcription

factors

-RRB-

of

metabolic

pathways

-LRB-

genes

in

total

-RRB-

represented

in

a

Boolean

rule-based

formalism

-LRB-

Covert

et

al.

-RRB-

The

regulatory

network

model

is

combined

with

an

existing

genomescale

metabolic

model

-LRB-

Duarte

et

al.

-RRB-

We

demonstrate

three

major

applications

of

the

model

using

the

rFBA

approach

to

predict

gene

expression

changes

and

growth

phenotypes

of

knockout

-LRB-

KO

-RRB-

strains

First

we

analyze

previously

published

genome-scale

gene

expression

and

protein

DNA

interaction

data

sets

in

a

model-driven

fashion

Second

we

demonstrate

the

ability

to

iteratively

expand

an

existing

model

based

on

comparisons

of

model

predictions

with

experimental

data

This

iterative

model

building

strategy

allows

identification

of

new

interactions

supported

by

multiple

Figure

A

schematic

drawing

of

the

approach

used

in

this

work

for

model-based

analysis

of

growth

phenotyping

and

gene

expression

data

to

identify

new

network

components

and

interactions

The

experimental

data

types

and

inclusion

of

approach

combines

in

silico

modeling

of

genome-scale

metabolic

and

regulatory

networks

with

analythese

interactions

into

the

regulatory

sis

of

in

vivo

data

obtained

by

gene

expression

and

growth

phenotyping

experiments

Specific

misprenetwork

model

Third

we

show

how

an

dictions

of

either

gene

expression

changes

or

growth

phenotypes

are

identified

and

used

as

inputs

for

systematic

model

expansion

The

primary

data

types

used

for

model

expansion

are

ChIP-chip

data

on

integrated

metabolic/regulatory

network

model

can

be

used

to

analyze

di

protein

DNA

interactions

and

the

presence

of

known

TF-binding

motifs

on

promoters

The

result

of

the

expansion

is

a

model

that

includes

new

regulatory

interactions

that

allow

improved

prediction

of

verse

data

types

such

as

phenotyping

expression

changes

and

growth

phenotypes

of

knockout

and

overexpression

strains

and

gene

expression

data

simultaneously

within

the

same

modeling

response

to

environmental

and

genetic

perturbations

and

inteframework

The

overall

approach

used

for

model-based

integragration

of

the

regulatory

network

to

the

metabolic

network

tion

of

diverse

data

sets

is

schematically

represented

in

Figure

model

as

described

previously

-LRB-

Covert

and

Palsson

Covert

et

al.

-RRB-

The

Supplemental

material

contains

all

the

interacResults

tions

included

in

the

model

the

Boolean

rules

describing

the

expression

of

each

target

gene

and

literature

references

Reconstructed

transcriptional

regulatory

network

Using

the

previously

described

genome-scale

compartmentalized

metabolic

model

of

S.

cerevisiae

-LRB-

Duarte

et

al.

-RRB-

as

a

starting

point

we

reconstructed

the

transcriptional

regulatory

network

regulating

all

of

the

metabolic

subsystems

included

in

The

resulting

integrated

metabolic/regulatory

network

accounts

for

genes

and

regulatory

interactions

The

network

consists

of

the

metabolic

genes

in

and

specific

nutrient-regulated

transcription

factors

-LRB-

TFs

-RRB-

The

model

allows

distinct

intra

and

extracellular

metabolites

to

act

as

input

signals

to

the

regulatory

network

The

direct

and

indirect

regulatory

interactions

for

each

gene

were

collected

from

primary

literature

and

they

are

based

on

detailed

studies

of

transcriptional

regulation

of

individual

genes

In

addition

to

representing

these

regulatory

interactions

also

includes

rules

describing

the

mode

of

combinatorial

control

by

different

TFs

at

each

promoter

This

logic-based

representation

allows

in

silico

prediction

of

gene

expression

changes

in

Prediction

of

gene

expression

changes

In

order

to

assess

the

completeness

of

the

network

described

above

we

compared

in

silico

gene

expression

change

predictions

to

experimentally

measured

expression

profiles

as

well

as

experimentally

determined

protein

DNA

interactions

-LRB-

chromatin

immunoprecipitation

followed

by

microarray

analysis

or

ChIP-chip

-RRB-

and

predicted

TF-binding

motifs

-LRB-

Harbison

et

al.

-RRB-

We

used

gene

expression

data

for

eight

transcription

factor

knockout

strains

-LRB-

-RRB-

and

two

overexpression

-LRB-

-RRB-

strains

from

previously

published

reports

-LRB-

Ideker

et

al.

Natarajan

et

al.

Ter

Linde

and

Steensma

Lascaris

et

al.

Young

et

al.

Kaniak

et

al.

-RRB-

Each

of

the

metabolic

genes

in

was

classified

as

significantly

up-regulated

significantly

down-regulated

or

unchanged

in

each

of

the

experimental

data

sets

The

in

silico

simulations

for

the

gene

expression

predictions

were

done

using

in

silico

environments

that

matched

the

experimental

conditions

for

each

individual

expression

data

set

The

ChIP-chip

and

motif

data

were

merged

to

obtain

a

set

of

potential

direct

target

genes

for

each

of

the

transcription

factors

listed

above

See

Reed

et

al.

-LRB-

-RRB-

for

conventions

used

for

naming

in

silico

strains

MH

refers

to

the

principal

person

responsible

for

reconstructing

the

model

is

the

number

of

genes

accounted

for

by

the

model

and

is

the

number

of

regulatory

interactions

in

the

model

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Regulatory

and

metabolic

networks

in

yeast

The

results

for

the

comparison

between

predictions

experimentally

observed

gene

expression

changes

and

experimentally

determined

protein

DNA

interactions

are

summarized

in

Figure

For

the

majority

of

genes

-LRB-

between

and

genes

-RRB-

in

each

of

the

strains

there

was

no

significant

expression

change

in

vivo

no

change

predicted

by

the

model

and

no

experimentally

determined

protein

DNA

interaction

between

the

transcription

factor

and

the

gene

Overall

out

of

the

predicted

gene

expression

changes

-LRB-

P

Fisher

's

exact

test

-RRB-

were

supported

by

both

gene

expression

and

ChIP-chip

motif

data

and

changes

-LRB-

P

-RRB-

were

supported

by

either

of

the

data

types

Figure

provides

a

summary

of

the

actual

data

that

were

used

to

derive

Figure

in

the

form

of

scatterplots

of

the

promoter

occupancy

scores

derived

from

ChIP-chip

data

plotted

against

gene

expression

changes

in

the

corresponding

TF

knockout

or

overexpression

strains

This

figure

shows

how

the

correlation

between

promoter

occupancy

and

expression

change

ranges

from

a

high

level

of

agreement

for

certain

TFs

-LRB-

and

-RRB-

to

almost

complete

lack

of

agreement

for

other

TFs

-LRB-

e.g.

and

-RRB-

As

Figure

shows

the

majority

of

the

comparisons

in

which

the

model

did

not

predict

an

expression

change

fell

into

three

categories

The

first

category

evidence

of

binding

in

the

ChIP-chip

data

but

no

gene

expression

change

-LRB-

cases

overall

-RRB-

includes

cases

in

which

there

are

likely

to

be

additional

regulators

acting

on

the

same

promoter

in

a

combinatorial

fashion

For

this

reason

expression

changes

are

not

observed

in

a

single

TF

knockout

or

overexpression

experiment

In

the

second

category

both

the

gene

expression

and

ChIP-chip

data

sets

agree

-LRB-

cases

overall

-RRB-

providing

strong

support

for

including

these

genes

as

novel

direct

targets

for

the

corresponding

TFs

In

the

third

and

largest

category

-LRB-

cases

overall

-RRB-

a

gene

expression

change

was

observed

but

there

was

no

evidence

of

direct

protein

DNA

binding

in

the

ChIP-chip

data

These

cases

correspond

to

suggested

indirect

targets

of

TFs

that

may

be

regulated

by

transcriptional

cascades

that

involve

two

or

more

TFs

Systematic

expansion

of

the

regulatory

network

Based

on

the

results

presented

above

it

is

clear

that

while

the

model

makes

relatively

few

gene

expression

change

predictions

that

were

not

supported

by

either

gene

expression

or

ChIP-chip

data

-LRB-

cases

overall

-RRB-

for

many

metabolic

genes

predicts

no

expression

change

even

in

cases

in

which

such

changes

are

observed

in

vivo

-LRB-

a

total

of

cases

across

all

strains

-RRB-

We

next

sought

to

improve

the

predictive

ability

of

the

in

silico

model

by

using

ChIP-chip

and

TF-binding

motif

data

-LRB-

Harbison

et

al.

-RRB-

to

systematically

expand

the

regulatory

network

part

of

the

model

First

the

gene

expression

comparison

presented

above

was

used

to

identify

potential

novel

candidate

target

genes

for

each

of

the

transcription

factors

-LRB-

TF-target

pairs

total

-RRB-

Next

we

traced

paths

through

an

expanded

regulatory

network

that

consisted

of

the

network

and

a

provisional

regulatory

network

that

can

be

established

based

on

combining

ChIP-chip

data

and

TF-binding

motif

data

-LRB-

Harbison

et

al.

-RRB-

We

used

a

systematic

search

algorithm

-LRB-

see

Methods

for

details

-RRB-

to

find

the

shortest

possible

path

from

a

particular

TF

to

a

particular

target

gene

through

this

combined

network

Figure

Comparison

between

expression

changes

in

TF

knockout

and

overexpression

strains

predicted

by

in

vivo

observed

expression

changes

and

promoter

occupancy

for

the

corresponding

TFs

derived

from

ChIP-chip

and

motif

data

-LRB-

A

-RRB-

Overlaps

between

the

three

data

sets

shown

in

the

form

of

Venn

diagrams

The

numbers

refer

to

the

number

of

genes

in

each

category

out

of

the

total

of

metabolic

genes

-LRB-

B

-RRB-

Interpretation

of

each

of

the

segments

in

the

Venn

diagrams

shown

in

A.

-LRB-

C

-RRB-

Scatterplots

of

the

gene

expression

changes

in

TF

knockout/overexpression

strains

-LRB-

ratios

between

knockout

strain

and

wild

type

-RRB-

and

the

corresponding

promoter

occupancy

scores

derived

from

ChIP-chip

data

-LRB-

of

the

P-value

reported

in

Harbison

et

al.

-LSB-

-RSB-

-RRB-

The

genes

that

were

predicted

to

change

in

expression

by

the

model

are

indicated

by

blue

circles

Genes

with

significant

gene

expression

change

or

promoter

occupancy

are

colored

using

a

color

scheme

similar

to

the

one

used

in

the

Venn

diagrams

in

A.

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Herrg

rd

et

al.

third

expansion

scenario

using

the

extended

TF

data

set

-LRB-

Fig.

-RRB-

allowed

reaching

of

the

target

genes

by

cascades

of

five

or

fewer

steps

-LRB-

P

-RRB-

The

third

expansion

scenario

suggested

several

previously

uncharacterized

regulatory

interactions

between

transcription

factors

that

would

allow

explaining

a

large

number

of

the

unaccounted

expression

changes

of

metabolic

genes

that

were

observed

in

vivo

The

novel

regulatory

interactions

that

were

most

commonly

used

in

the

regulatory

cascades

derived

from

the

expansion

approach

are

listed

in

Table

The

regulatory

interactions

that

were

identified

by

the

first

expansion

scenario

described

above

were

used

as

a

basis

for

building

an

improved

version

of

the

regulatory

network

We

analyzed

manually

each

of

the

suggested

regulatory

interactions

involving

the

TFs

in

the

original

model

and

included

the

interactions

that

did

not

cause

conflicts

with

existing

regulatory

rules

to

form

an

expanded

model

In

addition

minor

changes

to

existing

rules

in

the

model

were

made

to

remove

mispredictions

of

gene

expression

changes

due

to

incorrectly

represented

combinatorial

effects

especially

for

the

strain

In

order

to

provide

an

independent

test

set

to

assess

the

relative

performance

of

and

we

turned

to

prediction

of

gene

expression

changes

in

wild-type

yeast

strains

in

response

to

five

different

environmental

perturbations

-LRB-

Gasch

et

al.

Kwast

et

al.

-RRB-

As

Table

shows

overall

the

overlap

between

the

observed

and

predicted

expression

changes

for

genes

that

change

in

expression

either

in

silico

or

in

vivo

was

significant

in

each

of

the

five

conditions

-LRB-

P

Fisher

's

exact

test

-RRB-

The

model

showed

improved

predictive

ability

over

the

model

in

four

out

of

the

five

conditions

studied

Table

Regulatory

interactions

not

included

in

that

participate

in

or

more

regulatory

cascades

identified

by

the

network

expansion

approach

applied

to

the

full

ChIP-chip

data

set

with

transcription

factors

Number

of

cascades

Promoter

occupancy

score

Figure

Results

for

the

three

regulatory

network

expansion

scenarios

-LRB-

A

B

C

-RRB-

using

a

combination

of

the

network

and

a

provisional

regulatory

network

derived

from

ChIP-chip

and

TF-binding

motif

data

Each

pie

chart

indicates

the

fractions

of

potential

target

genes

that

can

be

reached

from

the

TF

through

regulatory

cascades

containing

one

to

five

steps

as

well

as

the

targets

that

can

not

be

reached

in

five

or

less

steps

from

the

TF

For

each

strain

the

numbers

of

potential

novel

targets

are

indicated

in

parenthesis

-LRB-

A

-RRB-

Expansion

using

ChIP-chip

and

motif

data

for

the

TFs

in

assuming

that

each

TF

can

only

act

as

a

repressor

or

activator

depending

on

its

known

type

of

activity

-LRB-

B

-RRB-

Expansion

using

the

same

ChIP-chip

and

motif

data

but

allowing

each

TF

to

act

either

as

repressor

or

activator

-LRB-

C

-RRB-

Expansion

using

ChIP-chip

and

motif

data

for

all

TFs

studied

in

Harbison

et

al.

-LRB-

-RRB-

allowing

each

TF

to

act

either

as

repressor

or

activator

We

investigated

three

different

network

expansion

scenarios

using

the

search

algorithm

In

the

first

scenario

we

only

used

ChIP-chip

and

motif

data

for

the

TFs

already

included

in

the

model

We

also

required

that

the

direction

of

the

expression

change

for

each

target

gene

would

be

correctly

explained

as

a

combination

of

the

regulatory

interactions

along

the

path

when

each

TF

was

considered

to

be

either

a

repressor

or

activator

depending

on

its

known

activity

In

the

second

scenario

we

used

the

same

set

of

ChIP-chip

and

motif

data

sets

but

we

relaxed

the

assumption

that

specific

transcription

factors

can

only

act

as

activators

or

repressors

In

the

third

scenario

we

included

ChIP-chip

and

motif

data

for

all

TFs

studied

in

Harbison

et

al.

-LRB-

-RRB-

again

allowing

each

TF

to

act

either

as

a

repressor

or

activator

The

three

panels

in

Figure

summarize

the

results

of

the

three

different

expansion

scenarios

for

each

TF

separately

as

well

as

for

the

whole

set

together

This

figure

shows

the

fractions

of

potential

target

genes

that

could

be

reached

after

a

certain

number

of

steps

in

a

regulatory

cascade

In

the

first

expansion

scenario

-LRB-

Fig.

-RRB-

of

the

potential

target

genes

across

all

the

strains

could

be

reached

through

transcriptional

regulatory

cascades

with

at

most

five

steps

-LRB-

P

compared

with

the

same

expansion

approach

applied

to

randomized

target

gene

sets

of

the

same

size

-RRB-

The

second

expansion

scenario

-LRB-

Fig.

-RRB-

did

not

significantly

increase

the

percentage

of

target

genes

that

could

be

reached

in

at

most

five

steps

-LRB-

P

-RRB-

In

contrast

the

Regulator

Target

Motif

Involving

only

metabolic

TFs

Involving

other

TFs

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

The

promoter

occupancy

score

is

-LRB-

P

-RRB-

where

P

is

the

P-value

reported

in

Harbison

et

al.

-LRB-

-RRB-

for

TF

promoter

interaction

based

on

their

error

model

to

analyze

the

ChIP-chip

data

The

motif

column

indicates

whether

a

binding

motif

for

the

regulator

was

found

on

the

promoter

of

the

target

gene

in

Harbison

et

al.

-LRB-

-RRB-

or

not

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Regulatory

and

metabolic

networks

in

yeast

Table

Comparison

between

gene

expression

changes

predicted

by

either

or

and

experimentally

observed

gene

expression

changes

in

response

to

environmental

perturbations

Correctly

predicted

change

Incorrectly

predicted

change

Change

not

predicted

No

change

predicted

or

observed

Data

set

Ethanol/glucose

Galactose/glucose

Anaerobic/aerobic

Nitrogen

depletion

Amino

acid

starvation

Total

Ethanol/glucose

Galactose/glucose

Anaerobic/aerobic

Nitrogen

depletion

Amino

acid

starvation

Total

P-value

92 69 632 236

388 489 648 581

575 2681 380 478

640 584 564 2646

The

P-value

reported

is

the

probability

of

observing

the

number

of

correctly

predicted

expression

changes

by

random

chance

given

the

total

number

of

changes

predicted

and

total

number

of

changes

observed

based

on

Fisher

's

exact

test

Prediction

of

growth

phenotypes

In

order

to

provide

data

for

an

evaluation

of

the

predictive

power

of

the

integrated

regulatory

and

metabolic

network

model

we

performed

quantitative

growth

phenotyping

experiments

on

different

carbon

sources

with

TF

deletion

strains

All

these

TFs

were

included

in

the

model

and

were

known

to

be

involved

in

carbon-source-dependent

transcriptional

regulation

or

otherwise

play

a

role

as

key

regulators

in

carbon

source

metabolism

The

carbon

sources

were

chosen

to

represent

both

fermentable

and

nonfermentable

carbon

sources

We

used

an

optimization-based

approach

-LRB-

see

Methods

-RRB-

to

compare

growth

phenotype

predictions

to

experimental

data

This

approach

allowed

us

to

systematically

identify

which

of

the

growth

phenotypes

are

unexpected

considering

the

current

understanding

of

the

regulatory

and

metabolic

networks

in

S.

cerevisiae

as

represented

by

The

predicted

growth

rates

are

shown

in

Figure

together

with

the

experimental

growth

rate

data

For

most

carbon

source/strain

combinations

the

model

predictions

are

quantitatively

in

good

agreement

with

the

experimental

data

However

we

identified

cases

where

the

growth

rates

were

significantly

overpredicted

in

silico

The

growth

rate

overpredictions

were

assumed

to

correspond

to

missing

regulatory

effects

in

the

model

and

hence

we

sought

to

identify

potential

novel

regulatory

mechanisms

that

would

improve

model

predictions

We

identified

metabolic

genes

whose

deletion

in

would

result

in

a

reduced

in

silico

growth

rate

prediction

compared

to

the

wild-type

strain

specifically

on

each

carbon

source

These

genes

were

then

considered

to

be

direct

or

indirect

candidate

target

genes

for

the

transcription

factors

whose

knockout

growth

phenotype

on

the

carbon

source

was

incorrectly

predicted

Next

we

used

gene

expression

data

sets

for

TF

deletion

strains

for

which

such

data

were

available

-LRB-

and

strains

-RRB-

to

further

narrow

down

the

set

of

potential

targets

by

only

including

genes

that

were

down-regulated

in

the

TF

KO

strain

in

vivo

Specific

new

direct

or

indirect

target

genes

that

resulted

in

improved

growth

rate

predictions

were

identified

in

five

of

the

cases

using

this

approach

After

including

the

suggested

new

regulatory

effects

in

only

eight

significant

mispredictions

remained

as

is

shown

in

Figure

and

overall

the

model

predicted

TF

KO

strain

growth

rates

more

accurately

than

before

Discussion

We

presented

a

literature-based

reconstruction

of

the

known

nutrient-controlled

transcriptional

regulatory

network

regulating

metabolism

in

S.

cerevisiae

and

the

integration

of

this

regulatory

network

with

the

genome-scale

metabolic

network

model

We

applied

previously

developed

in

silico

methods

for

combined

simulation

of

regulatory

and

metabolic

network

models

to

predict

expression

profiles

and

growth

phenotypes

Comparison

of

these

predictions

with

large-scale

experimental

data

allowed

identifying

regulatory

mechanisms

missing

from

the

model

and

expansion

of

the

model

by

using

existing

ChIP-chip

and

promoter

motif

data

A

similar

approach

for

integrated

modeling

was

first

applied

to

genome-scale

regulatory

and

metabolic

networks

in

E.

coli

-LRB-

Covert

and

Palsson

Covert

et

al.

-RRB-

but

in

yeast

the

availability

of

ChIP-chip

data

allows

integrating

this

key

data

type

into

the

network

expansion

approach

Similarly

to

the

case

of

the

initial

E.

coli

regulatory

network

reconstruction

-LRB-

Covert

et

al.

-RRB-

the

overall

agreement

between

in

silico

gene

expression

predictions

by

and

experimental

data

was

found

to

be

relatively

low

In

particular

a

large

number

of

the

experimentally

observed

expression

changes

were

not

predicted

by

However

the

agreement

between

model

predictions

and

the

experimental

data

sets

was

significantly

better

in

cases

in

which

the

gene

expression

and

ChIPchip

data

sets

were

in

good

agreement

with

each

other

-LRB-

Fig.

-RRB-

Conversely

when

the

agreement

between

gene

expression

and

ChIP-chip

data

was

poor

as

was

the

case

for

the

and

transcription

factors

the

model

predictions

were

usually

different

from

either

data

set

These

results

indicate

that

in

particular

regulatory

subsystems

the

expression

response

may

be

highly

condition

dependent

and

information

in

the

literature

derived

from

experiments

performed

under

diverse

conditions

may

not

allow

building

a

coherent

overall

picture

of

the

regulatory

network

For

example

the

nitrogen

regulation

network

in

which

and

act

as

key

regulators

is

known

to

consist

of

multiple

interlinked

negative

feedback

loops

that

allow

selective

re

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Herrg

rd

et

al.

expression

changes

that

were

not

predicted

by

the

model

may

be

stress

induced

Verifying

the

suggested

regulatory

interactions

between

metabolic

and

stress

response

TFs

listed

in

Table

through

additional

experimentation

would

allow

expanding

the

model

to

include

key

stress

response

pathways

and

their

connections

to

metabolic

processes

In

addition

to

stress

response

regulators

it

is

likely

that

multifunctional

transcriptional

regulators

such

as

or

-LRB-

Lieb

et

al.

Miyake

et

al.

-RRB-

and

chromatin-modifying

enzymes

-LRB-

Narlikar

et

al.

-RRB-

also

play

a

role

in

response

to

changes

in

the

nutritional

environment

of

the

cell

In

the

future

as

more

information

is

accumulated

about

the

regulation

of

the

activity

of

these

pleiotropic

regulators

we

hope

to

be

able

to

incorporate

them

into

transcriptionally

regulated

metabolic

models

The

set

of

new

regulatory

interactions

suggested

by

the

network

expansion

approach

was

used

to

develop

an

improved

metabolic/regulatory

network

model

The

predictions

of

growth

rates

of

TF

deletion

strains

made

by

the

model

were

in

good

agreement

with

experimentally

measured

growth

rates

for

most

TF/carbon

source

combinations

The

discrepancies

in

growth

phenotype

predictions

were

analyzed

using

a

model-based

approach

that

allowed

the

identification

of

potential

Figure

-LRB-

A

-RRB-

Measured

-LRB-

upper

corner

-RRB-

and

predicted

-LRB-

lower

corner

-RRB-

maximum

specific

growth

rates

new

regulatory

mechanisms

mediating

-LRB-

-RRB-

for

transcription

factor

deletion

strains

on

different

carbon

sources

The

in

silico

predictions

were

carbon

source

utilization

that

are

dismade

using

the

model

The

color

scheme

indicates

the

magnitude

of

the

growth

rate

cussed

in

detail

in

the

Supplemental

mafrom

low

-LRB-

dark

-RRB-

to

high

-LRB-

light

-RRB-

The

squares

with

numbers

in

bold/italics

indicate

strain-condition

terial

The

changes

to

the

model

identicombinations

with

significant

mispredictions

discussed

in

the

Supplemental

material

-LRB-

B

-RRB-

Comparison

of

experimentally

measured

and

predicted

growth

rates

after

the

improvements

to

the

model

disfied

through

this

process

were

modificacussed

in

the

Supplemental

material

were

done

tions

of

regulatory

mechanisms

for

specific

genes

involved

in

utilization

of

sponse

to

different

nitrogen

sources

-LRB-

Cooper

Magasanik

particular

carbon

sources

such

as

-LRB-

galactose

-RRB-

and

and

Kaiser

-RRB-

Additional

gene

expression

and

ChIP-chip

ex

-LRB-

mannose

-RRB-

periments

with

TF

overexpression

and

conditional

TF

mutants

Conclusions

performed

under

carefully

controlled

conditions

would

be

needed

to

map

the

targets

of

transcription

factors

in

such

comWe

have

described

the

reconstruction

of

the

nutrient-controlled

plex

subnetworks

transcriptional

regulatory

network

regulating

metabolism

in

S.

Using

ChIP-chip

and

motif

data

sets

-LRB-

Harbison

et

al.

-RRB-

cerevisiae

and

evaluation

of

the

predictive

power

of

an

integrated

to

systematically

expand

the

model

allowed

exmetabolic/regulatory

network

We

compared

model

predictions

plaining

one-third

of

the

gene

expression

prediction

discrepanto

experimental

gene

expression

data

and

identified

a

significant

cies

through

hypothetical

regulatory

cascades

involving

the

number

of

expression

changes

that

were

not

predicted

by

the

TFs

in

the

model

-LRB-

Fig.

-RRB-

Interestingly

including

ChIP-chip

model

A

combination

of

ChIP-chip

and

binding-site

motif

data

and

motif

data

for

additional

TFs

that

are

not

traditionally

aswas

used

to

expand

the

regulatory

network

model

so

that

its

sumed

to

be

key

regulators

of

metabolic

processes

almost

ability

to

predict

expression

changes

was

improved

in

independoubled

the

number

of

gene

expression

discrepancies

that

could

dent

test

cases

This

network

expansion

also

suggested

several

be

explained

through

regulatory

cascades

derived

from

the

data

novel

connections

between

nutrient

regulation

of

metabolism

-LRB-

Fig.

-RRB-

Such

cascades

commonly

involved

TFs

known

to

play

and

other

regulatory

subsystems

that

require

further

validation

a

role

in

response

to

various

cellular

stresses

such

as

through

detailed

experiments

It

was

found

that

the

integrated

and

-LRB-

Table

-RRB-

indicating

that

many

of

the

in

vivo

metabolic/regulatory

network

model

could

be

used

to

predict

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Regulatory

and

metabolic

networks

in

yeast

growth

phenotypes

and

the

discrepancies

in

these

predictions

could

be

used

to

direct

the

search

for

novel

regulatory

mechanisms

The

present

work

shows

how

a

systematic

approach

can

be

used

to

fill

in

missing

regulatory

mechanisms

through

the

combined

use

of

an

integrated

model

of

regulation

and

metabolism

and

existing

large-scale

experimental

data

sets

In

the

future

the

combination

of

more

targeted

expression

profiling

ChIPchip

growth

phenotyping

and

metabolic

flux

profiling

-LRB-

Blank

et

al.

-RRB-

under

many

different

environmental

conditions

will

allow

systematic

iterative

model

building

With

increasing

knowledge

of

specific

regulatory

mechanisms

in

higher

eukaryotes

-LRB-

Picard

et

al.

Wolfrum

et

al.

-RRB-

the

approaches

used

in

this

work

can

be

readily

extended

to

studying

the

network-level

interplay

between

regulation

and

metabolism

in

these

organisms

turn

used

to

calculate

the

changes

in

external

concentrations

and

these

concentrations

as

well

as

the

internal

fluxes

are

used

to

evaluate

the

rules

at

the

next

time

step

to

obtain

the

gene

expression

state

-LRB-

see

Supplemental

material

for

details

-RRB-

Model

parameter

estimation

There

are

several

parameters

such

as

maximum

substrate

and

oxygen

uptake

rates

that

affect

growth

rate

predictions

by

the

metabolic/regulatory

network

mode

For

the

growth

phenotyping

study

these

parameters

were

not

experimentally

measured

so

that

they

had

to

be

estimated

computationally

The

particular

estimation

strategy

used

in

this

work

is

based

on

genetic

algorithms

that

allow

optimization

of

a

nonlinear

objective

function

with

nonlinear

constraints

-LRB-

Goldberg

-RRB-

In

our

application

the

objective

was

to

minimize

the

summed

absolute

discrepancies

between

predicted

and

experimentally

measured

growth

rates

weighted

by

the

experimental

standard

deviations

of

the

growth

rates

under

the

growth

conditions

for

the

strains

The

Supplemental

material

contains

details

of

the

parameter

estimation

approach

and

sensitivity

analysis

of

the

growth

rate

predictions

Methods

Model

reconstruction

The

relevant

literature

for

each

metabolic

-LRB-

Duarte

et

al.

-RRB-

and

transcription

factor

gene

was

collected

through

information

in

the

SGD

YPD

and

MIPS

databases

-LRB-

Costanzo

et

al.

Christie

et

al.

Mewes

et

al.

-RRB-

and

direct

PubMed

searches

The

regulatory

network

model

part

of

consists

of

three

layers

describing

the

effect

of

extracellular

and

intracellular

signals

on

the

expression

of

metabolic

genes

and

the

maximum

allowed

flux

through

particular

reactions

in

the

network

The

first

layer

describes

the

activities

of

TFs

in

response

to

extracellular

and

intracellular

metabolite

concentrations

The

second

layer

consists

of

the

rules

describing

the

expression

of

metabolic

genes

as

a

function

of

the

transcription

factor

states

and

metabolite

concentrations

in

cases

in

which

the

direct

regulatory

mechanisms

were

unknown

For

the

remaining

metabolic

genes

no

information

on

regulation

could

be

found

in

the

literature

and

they

were

assumed

to

be

constitutively

expressed

in

all

environmental

conditions

The

third

layer

contains

the

gene

protein

reaction

associations

that

encode

the

relationship

between

gene

expression

and

presence/absence

of

a

particular

reaction

in

the

network

-LRB-

Duarte

et

al.

-RRB-

All

the

three

layers

were

implemented

as

Boolean

rules

derived

from

primary

literature

and

these

rules

are

listed

in

the

Supplemental

material

Gene

expression

data

comparisons

The

individual

gene

expression

data

sets

for

the

transcription

factor

knockout

or

overexpression

strains

and

wild-type

condition

shifts

were

obtained

directly

from

the

Supplemental

Web

sites

for

individual

papers

-LRB-

Gasch

et

al.

Ideker

et

al.

Natarajan

et

al.

Kwast

et

al.

Lin

et

al.

Ter

Linde

and

Steensma

Young

et

al.

Kaniak

et

al.

-RRB-

All

the

data

sets

were

first

converted

into

log

ratios

between

gene

expression

levels

in

the

knockout

or

overexpression

strain

and

in

the

wild-type

strain

or

between

two

conditions

The

criteria

that

were

used

to

find

significantly

up/down-regulated

genes

in

each

gene

expression

data

set

and

the

conditions

used

for

the

in

silico

simulations

of

the

TF

knockout

effects

on

gene

expression

are

listed

in

the

Supplemental

material

Network

expansion

The

network

expansion

approach

used

in

this

work

is

purely

graph-based

that

is

the

regulatory

network

was

represented

as

a

directed

graph

with

labeled

edges

indicating

either

repression

or

activation

of

a

target

gene

by

a

TF

or

a

regulatory

signal

The

provisional

network

derived

from

ChIP-chip

and

motif

data

-LRB-

Harbison

et

al.

-RRB-

as

described

in

the

Supplemental

material

was

combined

with

the

regulatory

network

in

and

each

of

the

edges

was

labeled

as

either

repressing

or

activating

depending

on

the

known

primary

function

of

the

TF

involved

in

the

interaction

Next

a

breadth

first

search

was

used

to

find

all

directed

noncyclic

paths

up

to

a

certain

number

of

steps

in

the

combined

network

that

connect

a

transcription

factor

to

a

particular

potential

target

gene

In

the

first

expansion

scenario

discussed

in

the

Results

section

only

paths

that

would

result

in

the

correct

sign

for

the

expression

change

of

the

target

gene

in

the

TF

KO

or

overexpression

strain

were

considered

further

In

the

other

two

expansion

scenarios

this

path

coherence

criterion

was

not

used

In

order

to

establish

the

final

expanded

model

the

interactions

in

the

shortest

coherent

path

for

each

TF

target

pair

were

included

in

a

provisional

regulatory

network

model

The

suggested

new

interactions

were

then

integrated

with

existing

rules

by

manual

inspection

The

predictions

of

expression

changes

in

all

modified

strains

were

then

re-run

with

the

new

model

and

interactions

that

reduced

the

overall

predictive

power

of

the

model

were

removed

from

the

model

All

the

computations

presented

in

this

paper

were

done

using

Matlab

-LRB-

Mathworks

Regulated

flux

balance

analysis

The

constraint-based

modeling

approach

for

genome-scale

metabolic

networks

has

been

extensively

reviewed

elsewhere

-LRB-

Price

et

al.

-RRB-

and

will

not

be

described

in

full

detail

here

The

metabolic

network

model

consists

of

the

stoichiometric

matrix

describing

all

the

interconversions

between

metabolites

in

the

network

maximum

flux

capacity

constraints

on

all

reactions

and

a

cellular

objective

function

to

be

maximized

-LRB-

based

on

the

estimated

biomass

composition

-RRB-

The

integration

of

the

metabolic

network

analysis

and

the

transcriptional

regulatory

network

model

described

above

is

accomplished

using

the

regulated

flux

balance

analysis

approach

-LRB-

Covert

et

al.

-RRB-

In

this

approach

the

three

layers

of

rules

detailed

above

are

evaluated

given

an

initial

condition

-LRB-

extracellular

concentrations

and

intracellular

fluxes

-RRB-

and

the

state

of

each

reaction

in

the

metabolic

model

is

calculated

The

maximum

fluxes

through

the

reactions

that

are

determined

to

be

down-regulated

are

then

set

to

a

predetermined

fraction

of

the

unregulated

maximum

flux

Given

the

stoichiometric

matrix

an

objective

function

and

the

new

maximum

flux

capacities

a

particular

flux

distribution

can

be

calculated

using

linear

optimization

The

resulting

flux

distribution

is

then

in

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Herrg

rd

et

al.

Inc.

-RRB-

and

the

FBA

problems

were

solved

using

the

Lindo

linear

programming

solver

-LRB-

Lindo

Systems

Inc.

-RRB-

Natl.

Acad

Sci

Gasch

A.P.

Spellman

P.T.

Kao

C.M.

Carmel-Harel

O.

Eisen

M.B.

Storz

G.

Botstein

D.

and

Brown

P.O.

Genomic

expression

programs

in

the

response

of

yeast

cells

to

environmental

changes

Mol

Biol

Cell

Gavin

A.C.

Aloy

P.

Grandi

P.

Krause

R.

Boesche

M.

Marzioch

M.

Rau

C.

Jensen

L.J.

Bastuck

S.

Dumpelfeld

B.

et

al.

Proteome

survey

reveals

modularity

of

the

yeast

cell

machinery

Nature

-LRB-

in

press

-RRB-

Giaever

G.

Chu

A.M.

Ni

L.

Connelly

C.

Riles

L.

Veronneau

S.

Dow

S.

Lucau-Danila

A.

Anderson

K.

Andre

B.

et

al.

Functional

profiling

of

the

Saccharomyces

cerevisiae

genome

Nature

Goldberg

D.E.

Genetic

algorithms

in

search

optimization

and

machine

learning

Addison-Wesley

Reading

MA

Grunenfelder

B.

and

Winzeler

E.A.

Treasures

and

traps

in

genome-wide

data

sets

Case

examples

from

yeast

Nat

Rev.

Genet

Harbison

C.T.

Gordon

D.B.

Lee

T.I.

Rinaldi

N.J.

Macisaac

K.D.

Danford

T.W.

Hannett

N.M.

Tagne

J.B.

Reynolds

D.B.

Yoo

J.

et

al.

Transcriptional

regulatory

code

of

a

eukaryotic

genome

Nature

Herrg

rd

M.J.

Covert

M.W.

and

Palsson

B.

Reconstruction

of

microbial

transcriptional

regulatory

networks

Curr

Opin

Biotechnol

Ideker

T.

Thorsson

V.

Ranish

J.A.

Christmas

R.

Buhler

J.

Eng

J.K.

Bumgarner

R.

Goodlett

D.R.

Aebersold

R.

and

Hood

L.

Integrated

genomic

and

proteomic

analyses

of

a

systematically

perturbed

metabolic

network

Science

Kaniak

A.

Xue

Z.

Macool

D.

Kim

J.H.

and

Johnston

M.

Regulatory

network

connecting

two

glucose

signal

transduction

pathways

in

Saccharomyces

cerevisiae

Eukaryot

Cell

Kauffman

K.J.

Prakash

P.

and

Edwards

J.S.

Advances

in

flux

balance

analysis

Curr

Opin

Biotechnol

Kwast

K.E.

Lai

L.C.

Menda

N.

James

III

D.T.

Aref

S.

and

Burke

P.V.

Genomic

analyses

of

anaerobically

induced

genes

in

Saccharomyces

cerevisiae

Functional

roles

of

and

other

factors

in

mediating

the

anoxic

response

J.

Bacteriol

Lascaris

R.

Bussemaker

H.J.

Boorsma

A.

Piper

M.

van

der

Spek

H.

Grivell

L.

and

Blom

J.

overexpression

in

glucose-grown

Saccharomyces

cerevisiae

induces

cells

to

enter

a

novel

metabolic

state

Genome

Biol

Lieb

J.D.

Liu

X.

Botstein

D.

and

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P.O.

Promoter-specific

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of

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by

genome-wide

maps

of

protein

DNA

association

Nat

Genet

Lin

S.J.

Kaeberlein

M.

Andalis

A.A.

Sturtz

L.A.

Defossez

P.A.

Culotta

V.C.

Fink

G.R.

and

Guarente

L.

Calorie

restriction

extends

Saccharomyces

cerevisiae

lifespan

by

increasing

respiration

Nature

Magasanik

B.

and

Kaiser

C.A.

Nitrogen

regulation

in

Saccharomyces

cerevisiae

Gene

Mewes

H.W.

Amid

C.

Arnold

R.

Frishman

D.

Guldener

U.

Mannhaupt

G.

Munsterkotter

M.

Pagel

P.

Strack

N.

Stumpflen

V.

et

al.

MIPS

Analysis

and

annotation

of

proteins

from

whole

genomes

Nucleic

Acids

Res

Miyake

T.

Reese

J.

Loch

C.M.

Auble

D.T.

and

Li

R.

Genome-wide

analysis

of

ARS

-LRB-

autonomously

replicating

sequence

-RRB-

binding

factor

-LRB-

-RRB-

mediated

transcriptional

regulation

in

Saccharomyces

cerevisiae

J.

Biol

Chem

Narlikar

G.J.

Fan

H.Y.

and

Kingston

R.E.

Cooperation

between

complexes

that

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chromatin

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and

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Natarajan

K.

Meyer

M.R.

Jackson

B.M.

Slade

D.

Roberts

C.

Hinnebusch

A.G.

and

Marton

M.J.

Transcriptional

profiling

shows

that

is

a

master

regulator

of

gene

expression

during

amino

acid

starvation

in

yeast

Mol

Cell

Biol

Picard

F.

Kurtev

M.

Chung

N.

Topark-Ngarm

A.

Senawong

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Machado

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Oliveira

R.

Leid

M.

McBurney

M.W.

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N.D.

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J.L.

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Genome-scale

models

of

microbial

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the

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of

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Rev.

Microbiol

Reed

J.L.

Vo

T.D.

Schilling

C.H.

and

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B.

An

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genome-scale

model

of

Escherichia

coli

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GSM/GPR

-RRB-

Genome

Biol

Sauer

U.

High-throughput

phenomics

Experimental

methods

for

mapping

fluxomes

Curr

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Biotechnol

Ter

Linde

J.J.

and

Steensma

H.Y.

A

microarray-assisted

screen

for

potential

and

target

genes

in

Saccharomyces

cerevisiae

Yeast

Experimental

protocols

The

wild-type

S.

cerevisiae

haploid

strain

used

in

this

study

-LRB-

MATa

-RRB-

was

obtained

from

ATCC

All

the

single

transcription

factor

knockout

strains

were

from

the

S.

cerevisiae

gene

deletion

collection

-LRB-

Winzeler

et

al.

-RRB-

and

were

generously

provided

by

T.

Ideker

Growth

on

different

carbon

sources

was

evaluated

by

using

the

Bioscreen

C

system

-LRB-

Thermo

Labsystems

-RRB-

Minimal

medium

-LRB-

Van

Hoek

et

al.

-RRB-

with

the

appropriate

auxotrophic

supplements

added

was

used

to

test

growth

on

different

carbon

sources

-LRB-

glucose

galactose

fructose

sucrose

mannose

ribose

lactate

pyruvate

ethanol

raffinose

glycerol

-RRB-

for

each

of

the

knockout

strains

as

well

as

the

wild-type

strain

Maximum

specific

growth

rates

were

determined

for

each

strain

carbon

source

combination

based

on

five

independent

measurements

as

described

in

the

Supplemental

material

Acknowledgments

We

thank

Trey

Ideker

Bing

Ren

Shankar

Subramaniam

George

Santangelo

and

Timothy

Hughes

for

valuable

discussions

We

also

thank

Iman

Famili

Natalie

Duarte

and

Monica

Mo

for

help

with

the

yeast

metabolic

model

and

Thuy

Vo

for

critical

comments

on

the

manuscript

Support

for

this

work

was

provided

by

the

National

Institutes

of

Health

-LRB-

-RRB-

and

National

Science

Foundation

-LRB-

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in

yeast

Van

Hoek

P.

Van

Dijken

J.P.

and

Pronk

J.T.

Effect

of

specific

growth

rate

on

fermentative

capacity

of

baker

's

yeast

Appl

Environ

Microbiol

Villas-Boas

S.G.

Moxley

J.F.

Akesson

M.

Stephanopoulos

G.

and

Nielsen

J.

High-throughput

metabolic

state

analysis

The

missing

link

in

integrated

functional

genomics

of

yeasts

Biochem

J.

Winzeler

E.A.

Shoemaker

D.D.

Astromoff

A.

Liang

H.

Anderson

K.

Andre

B.

Bangham

R.

Benito

R.

Boeke

J.D.

Bussey

H.

et

al.

Functional

characterization

of

the

S.

cerevisiae

genome

by

gene

deletion

and

parallel

analysis

Science

Wolfrum

C.

Asilmaz

E.

Luca

E.

Friedman

J.M.

and

Stoffel

M.

regulates

lipid

metabolism

and

ketogenesis

in

the

liver

during

fasting

and

in

diabetes

Nature

Young

E.T.

Dombek

K.M.

Tachibana

C.

and

Ideker

T.

Multiple

pathways

are

co-regulated

by

the

protein

kinase

and

the

transcription

factors

and

J.

Biol

Chem

Received

April

accepted

in

revised

form

February

Genome

Research

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O B O B O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

regulation network yeast transcription metabolism


brief communications Lethality and centrality in protein networks The most highly connected proteins in the cell are the most important for its survival . roteins are traditionally identified on the basis of their individual actions as catalysts , signalling molecules , or building blocks in cells and microorganisms . But our post-genomic view is expanding the protein 's role into an element in a network of protein protein interactions as well , in which it has a contextual or cellular function within functional modules1 ,2 . Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions . The S. cerevisiae protein protein interaction network we investigate has 1,870 proteins as nodes , connected by 2,240 identified direct physical interactions , and is derived from combined , non-overlapping data3 ,4 , obtained mostly by systematic twohybrid analyses3 . Owing to its size , a complete map of the network -LRB- Fig. 1a -RRB- , although informative , in itself offers little insight into its large-scale characteristics . Our first goal was therefore to identify the architecture of this network , determining whether it is best described by an inherently uniform exponential topology , with proteins on average possessing the same number of links , or by a highly heterogeneous scale-free topology , in which proteins have widely different connectivities5 . As we show in Fig. 1b , the probability that a given yeast protein interacts with k other yeast proteins follows a power law5 with an exponential cut-off 6 at kc 20 , a topology that is also shared by the protein protein interaction network of the bacterium Helicobacter pylori 7 . This indicates that the network of protein interactions in two separate organisms forms a highly inhomogeneous scale-free network in which a few highly connected proteins play a central role in mediating interactions among numerous , less connected proteins . An important known consequence of the inhomogeneous structure is the network 's simultaneous tolerance to random errors , coupled with fragility against the removal of the most connected nodes8 . We find that random mutations in the genome of S. cerevisiae , modelled by the removal of randomly selected yeast proteins , do not affect the overall topology of the network . By contrast , when the most connected proteins are computationally eliminated , the network diameter increases rapidly . This simulated tolerance against random mutation is in agreement with results from systematic mutagenesis P Figure 1 Characteristics of the yeast proteome . a , Map of protein protein interactions . The largest cluster , which contains ~ 78 % of all proteins , is shown . The colour of a node signifies the phenotypic effect of removing the corresponding protein -LRB- red , lethal ; green , nonlethal ; orange , slow growth ; yellow , unknown -RRB- . b , Connectivity distribution P -LRB- k -RRB- of interacting yeast proteins , giving the probability that a given protein interacts with k other proteins . The exponential cut-off 6 indicates that the number of proteins with more than 20 interactions is slightly less than expected for pure scale-free networks . In the absence of data on the link directions , all interactions have been considered as bidirectional . The parameter controlling the short-length scale correction has value k0 1 . c , The fraction of essential proteins with exactly k links versus their connectivity , k , in the yeast proteome . The list of 1,572 mutants with known phenotypic profile was obtained from the Proteome database13 . Detailed statistical analysis , including r 0.75 for Pearson 's linear correlation coefficient , demonstrates a positive correlation between lethality and connectivity . For additional details , see http://www.nd.edu/~networks/cell . experiments , which identified a striking capacity of yeast to tolerate the deletion of a substantial number of individual proteins from its proteome9 ,10 . However , if this is indeed due to a topological component to error tolerance , then , on average , less connected proteins should prove to be less essential than highly connected ones . To test this , we rank-ordered all interacting proteins based on the number of links they have , and correlated this with the phenotypic effect of their individual removal from the yeast proteome . As shown in Fig. 1c , the likelihood that removal of a protein will prove lethal correlates with the number of interactions the protein has . For example , although proteins with five or fewer links constitute about 93 % of the total number of proteins , we find that only about 21 % of them are essential . By contrast , only some 0.7 % of the yeast proteins with known phenotypic profiles have more than 15 links , but single deletion of 62 % or so of these proves lethal . This implies that highly connected proteins with a central role in the network 's architecture are three times more likely to be essential than proteins with only a small number of links to other proteins . 2001 Macmillan Magazines Ltd The simultaneous emergence of an inhomogeneous structure in both metabolic5 ,11 and protein interaction networks suggests that there has been evolutionary selection of a common large-scale structure of biological networks and indicates that future systematic protein protein interaction studies in other organisms will uncover an essentially identical protein-network topology . The correlation between the connectivity and indispensability of a given protein confirms that , despite the importance of individual biochemical function and genetic redundancy , the robustness against mutations in yeast is also derived from the organization of interactions and the topological positions of individual proteins12 . A better understanding of cell dynamics and robustness will be obtained from an integrated approach that simultaneously incorporates the individual and contextual properties of all constituents in complex cellular networks . H. Jeong * , S. P. Mason , A.-L . Barab si * , Z. N. Oltvai * Department of Physics , University of Notre Dame , Notre Dame , Indiana 46556 , USA e-mail : alb@nd.edu , zno008@nwu.edu 41 NATURE | VOL 411 | 3 MAY 2001 | www.nature.com brief communications Department of Pathology , Northwestern University Medical School , Chicago , Illinois 60611 , USA 1 . Hartwell , L. H. , Hopfield , J. J. , Leibler , S. & Murray , A. W. Nature 402 , 47 52 -LRB- 1999 -RRB- . 2 . Eisenberg , D. , Marcotte , E. M. , Xenarios , I. & Yeates , T. O. Nature 405 , 823 826 -LRB- 2000 -RRB- . 3 . Uetz , P. et al. . Nature 403 , 623 627 -LRB- 2000 -RRB- . 4 . Xenarios , I. et al. . Nucleic Acids Res . 28 , 289 291 -LRB- 2000 -RRB- . 5 . Jeong , H. , Tombor , B. , Albert , R. , Oltvai , Z. N. & Barab si , A.-L . Nature 407 , 651-654 -LRB- 2000 -RRB- . 6 . Amaral , L. A. , Scala , A. , Barthelemy , M. & Stanley , H. E. Proc . Natl Acad . Sci . USA 97 , 11149 11152 -LRB- 2000 -RRB- . 7 . Rain , J.-C . et al. . Nature 409 , 211 215 -LRB- 2001 -RRB- . 8 . Albert , R. , Jeong , H. & Barab si , A.-L . Nature 406 , 378 382 -LRB- 2000 -RRB- . 9 . Winzeler , E. A. et al. . Science 285 , 901 906 -LRB- 1999 -RRB- . 10 . Ross-Macdonald , P. et al. . Nature 402 , 413 418 -LRB- 1999 -RRB- . 11 . Fell , D. A. & Wagner , A. in Animating the Cellular Map -LRB- eds Hofmeyr , J.-H. , Rohwer , J. M. & Snoep , J. L. -RRB- 79 85 -LRB- Stellenbosch Univ. . Press , 2000 -RRB- . 12 . Wagner , A. Nature Genet . 24 , 355 361 -LRB- 2000 -RRB- . 13 . Costanzo , M. C. et al. . Nucleic Acids Res . 28 , 73 76 -LRB- 2000 -RRB- . Cell culture Progenitor cells from human brain after death ulturing neural progenitor cells from the adult rodent brain has become routine1 ,2 and is also possible from human fetal tissue3 ,4 , but expansion of these cells from postnatal and adult human tissue , although preferred for ethical reasons , has encountered problems5 8 . Here we describe the isolation and successful propagation of neural progenitor cells from human postmortem tissues and surgical specimens . Although the relative therapeutic merits of adult and fetal progenitor cells still need to be assessed , our results may extend the application of these progenitor cells in the treatment of neurodegenerative diseases . As a source of neural progenitor cells , we used brain tissue from an 11-week-old postnatal male , who died of extracerebral complications of myofibromatosis , and from a resectioned temporal cortex from a 27-yearold male -LRB- courtesy of J. Alksney -RRB- . The tissue was removed , sectioned 2 hours after death and placed in cold antibiotic-containing Hank 's buffered-salt solution , then processed for culture 3 hours later . Representative sections of hippocampus , ventricular zone , motor cortex and corpus callosum were taken . The temporal cortex tissue was provided en bloc and placed in chilled Hank 's buffered-salt solution and processed for culture 3 hours after removal . The adult tissues were divided into hippocampal formation , white matter and remaining cortical grey matter . Tissues were finely diced and then dissociated by enzymic digestion with papain , DNase I and neutral protease for 45 min at 37 C , as described for rodent tissue1 . We initially plated isolated cells onto fibronectin-coated plates in DMEM : F-12 medium containing glutamine , amphotericin-B , penicillin , streptomycin and 10 % fetal bovine serum . After 24 hours , the medium was replaced with DMEM : F12 supplemented with BIT-9500 -LRB- bovine serum albumin , transferrin , insulin ; Stem Cell Technologies -RRB- , 20 ng ml 1 basic fibroblast growth factor -LRB- FGF-2 -RRB- , 20 ng ml 1 epidermal growth factor , and 20 ng ml 1 platelet-derived growth factor AB . We had limited success using these condi - C tions , but when the medium was supplemented with 25 % conditioned medium from rat stem cells that had been genetically modified to overproduce a secreted form of FGF-2 and its stem-cell cofactor , the glycosylated form of cystatin C -LRB- ref . 9 -RRB- , plating efficiency , as well as initial survival and growth , greatly improved . The medium was changed every two days and the cultures were replated onto twice the surface area to accommodate proliferative expansion . All tissue samples gave neural progenitor cells , but the highest yields were from hippocampus and ventricular zone . For long-term storage , cultures were a b dissociated with trypsin , rinsed and cryopreserved in growth medium -LRB- without growth factors -RRB- containing 10 % dimethylsulphoxide . Cells from the 11-week-old tissue grew at log phase for more than 70 population doublings before showing signs of in vitro senescence -LRB- a significant reduction in growth rate -RRB- . The adult tissues were expanded for more than 30 doublings before senescence -LRB- Fig. 1 -RRB- . Neurons were spontaneously generated at all stages in these cultures -LRB- Fig. 1a , b -RRB- , and more complete differentiation could be induced by withdrawing growth factors and stimulating the cells with forskolin and retinoic acid -LRB- Fig. 1c -RRB- 1,9 . Neonatal and adult cultures produced similar proportions of neurons and astrocytes , although the number of spontaneously generated neurons was lower than reported for fetal cultures3 ,4 and decreased significantly as cultures reached senescence . Oligodendrocytes were rare in most cultures . So far , we have processed 23 tissue samples from people of different age groups . Most samples have yielded viable progenitor cells , with the longest post-mortem interval c d e f d7 g h d14 i d21 j Esitmated cells / doublings 1014 1012 1010 108 106 104 102 100 0 Neonatal k Esitmated cells / doublings 1014 10 12 Adult l 100 10 Neonatal Adult % Positive 1010 108 106 104 102 1 0.1 0.01 0.001 - tubulin NeuN GFAP O4 100 200 300 Days in culture 0 100 200 300 Days in culture Cell number Doublings Phenotype Figure 1 Neural progenitor cells isolated from postmortem human brain . a i , Cells obtained from brains of two males : an 11-week-old neonate -LRB- a c -RRB- and a 27-year-old adult -LRB- d i -RRB- . Proliferative cells -LRB- a , b -RRB- were stained1 for immature neurons -LRB- - tubulin ; green -RRB- , astrocytes -LRB- GFAP , glial fibrillary acidic protein ; red -RRB- and nuclei -LRB- DAPI stain ; blue -RRB- . Differentiated cells -LRB- c -RRB- were stained for neurons : Map2abc -LRB- red -RRB- and neurofilament 150 -LRB- green -RRB- , for GFAP -LRB- blue -RRB- , and nuclei -LRB- white -RRB- . Adult cells as seen by phase-contrast microscopy at days -LRB- d -RRB- 7 , 14 and 21 after plating -LRB- d-f -RRB- . Differentiated cells are shown in the third row : g , fibronectin -LRB- fibroblasts , red -RRB- , - tubulin -LRB- green -RRB- , GFAP -LRB- blue -RRB- , nuclei -LRB- white -RRB- ; h , Map2abc -LRB- green -RRB- , GFAP -LRB- blue -RRB- , nuclei -LRB- white -RRB- ; i , immature glia -LRB- A2B5 , green -RRB- , GFAP -LRB- blue -RRB- , nuclei -LRB- white -RRB- . The growth of neonatal and adult cells in culture is shown in the bottom row : j , k , phenotypic counts of differentiated neonatal and adult cells , respectively ; l , immunostaining of differentiated cells : NeuN , post-mitotic neuron marker ; O4 , immature oligodendrocyte marker ; fibronectin , connective tissue . 2001 Macmillan Magazines Ltd NATURE | VOL 411 | 3 MAY 2001 | www.nature.com 42 Fibronectin 100

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