Causal

Reasoning

in

Rats

Aaron

P.

Blaisdell

et

al.

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Neural

Transm

Gen.

Sect.

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We

thank

W.

Curtis

for

maintaining

the

DBH

mouse

colony

Sumitomo

Pharmaceuticals

for

DOPS

C.

Hutter

for

statistical

advice

P.

M.

Tran

for

technical

assistance

and

our

colleagues

for

constructive

suggestions

Supported

by

an

NIH

National

Research

Service

Award

grant

-LRB-

V.G.O.

-RRB-

and

an

institutional

Program

Project

grant

from

the

National

Institute

on

Drug

Abuse

Supporting

Online

Material

Materials

and

Methods

Figs

to

August

accepted

January

science

Causal

Reasoning

in

Rats

Aaron

P.

Blaisdell

Kosuke

Sawa

Kenneth

J.

Leising

Michael

R.

Empirical

research

with

nonhuman

primates

appears

to

support

the

view

that

causal

reasoning

is

a

key

cognitive

faculty

that

divides

humans

from

animals

The

claim

is

that

animals

approximate

causal

learning

using

associative

processes

The

present

results

cast

doubt

on

that

conclusion

Rats

made

causal

inferences

in

a

basic

task

that

taps

into

core

features

of

causal

reasoning

without

requiring

complex

physical

knowledge

They

derived

predictions

of

the

outcomes

of

interventions

after

passive

observational

learning

of

different

kinds

of

causal

models

These

competencies

can

not

be

explained

by

current

associative

theories

but

are

consistent

with

causal

Bayes

net

theories

he

ability

to

acquire

and

reason

with

causal

knowledge

is

among

our

most

central

human

cognitive

competences

-LRB-

-RRB-

Causal

knowledge

serves

two

important

functions

It

allows

us

to

predict

outcomes

on

the

basis

of

observations

and

it

underlies

our

ability

to

control

events

in

the

world

We

investigated

whether

animals

understand

the

relation

between

observations

and

interventions

which

some

philosophers

regard

as

a

core

feature

of

causal

reasoning

-LRB-

-RRB-

Although

a

number

of

psychologists

have

claimed

that

both

humans

and

animals

use

basic

associative

mechanisms

to

learn

about

causal

relations

-LRB-

-RRB-

human

studies

have

demonstrated

a

deeper

understanding

of

causal

relations

that

can

not

be

reduced

to

associative

learning

-LRB-

-RRB-

In

contrast

research

on

the

cognitive

competencies

of

nonhuman

primates

concludes

that

they

demonstrate

a

superficial

understanding

of

the

association

between

tool

use

and

its

effects

but

fail

to

comprehend

the

unobservable

physical

mechanisms

underlying

these

relations

E

-LRB-

-RRB-

but

see

-LRB-

-RRB-

It

may

well

be

however

that

nonhuman

animals

lack

knowledge

about

physical

mechanisms

but

still

are

capable

T

of

basic

causal

reasoning

The

capacity

to

derive

predictions

for

interventions

after

purely

observational

learning

is

a

core

competency

that

is

not

reducible

to

associative

learning

-LRB-

-RRB-

Humans

and

animals

can

learn

associations

between

passively

observed

events

-LRB-

Pavlovian

conditioning

-RRB-

as

well

as

between

interventions

and

outcomes

-LRB-

instrumental

conditioning

-RRB-

Moreover

these

two

learning

modes

may

interact

-LRB-

-RRB-

An

understanding

of

the

interrelations

between

observations

-LRB-

Bseeing

-LSB-

-RRB-

and

interventions

-LRB-

Bdoing

-LSB-

-RRB-

however

requires

more

sophisticated

representations

Simple

transfer

from

observational

learning

can

lead

to

inadequate

predictions

for

interventions

For

example

barometer

readings

statistically

predict

the

weather

but

at

the

same

time

setting

the

barometer

to

an

arbitrary

reading

does

not

influence

the

weather

Both

relations

could

be

learned

with

associative

mechanisms

in

separate

observational

and

instrumental

learning

trials

but

associative

theories

are

incapable

of

deriving

correct

predictions

for

interventions

after

observational

learning

when

no

prior

instrumental

learning

is

available

The

causal

model

in

Fig.

shows

how

predictions

for

interventions

can

be

derived

from

observations

Imagine

that

an

animal

learns

in

an

observational

Pavlovian

learning

phase

that

a

light

cue

-LRB-

L

-RRB-

temporally

precedes

both

a

tone

stimulus

-LRB-

T

-RRB-

and

food

-LRB-

F

-RRB-

thus

learning

a

common-cause

model

with

two

effects

-LRB-

top

panel

-RRB-

After

learning

this

model

observing

T

should

via

L

lead

to

the

predictive

inference

that

F

should

also

be

present

However

if

the

animal

learns

in

the

test

phase

that

a

newly

introduced

lever

turns

on

T

it

should

be

more

Department

of

Psychology

University

of

California

Los

Angeles

CA

USA

Society

for

the

Promotion

of

Science

Nagoya

University

Nagoya

Japan

Department

of

Psychology

University

of

Gottingen

Gottingen

Germany

To

whom

correspondence

should

be

addressed

E-mail

blaisdell@psych.ucla.edu

Fig.

-LRB-

A

-RRB-

Causal

model

used

in

experiment

Intervene

L

p

L

-LRB-

light

-RRB-

is

the

common

Observe

cause

of

T

-LRB-

tone

-RRB-

and

F

p

-LRB-

food

-RRB-

N

-LRB-

noise

-RRB-

is

the

T

F

N

direct

cause

of

F.

-LRB-

Top

panel

-RRB-

Observed

causal

relations

-LRB-

Bottom

el

-RRB-

Model

modified

unIntervening

der

the

assumption

of

L

an

intervention

in

T

and

N.

-LRB-

B

-RRB-

Experiment

Common-Cause

Direct-Cause

Mean

nose

pokes

in

N

T

F

Training

response

to

test

stimulus

T

-LRB-

P

G

-RRB-

in

the

common-cause

condition

and

to

N

-LRB-

P

-RRB-

in

the

direct-cause

condition

after

a

lever

press

-LRB-

intervene

-RRB-

or

no

lever

press

-LRB-

observe

-RRB-

Bars

indicate

SEM

Planned

comparisons

from

a

two-way

mixed

analysis

of

variance

-LRB-

ANOVA

-RRB-

are

shown

There

was

a

main

effect

of

causal

model

-LRB-

common

or

direct

-RRB-

F

-LRB-

-RRB-

P

G

and

an

interaction

between

causal

model

and

test

condition

-LRB-

intervene

or

observe

-RRB-

F

-LRB-

-RRB-

P

Observing

A

B

FEBRUARY

VOL

SCIENCE

Mean

nose

pokes

Downloaded

from

on

May

REPORTS

reluctant

to

predict

F

-LRB-

bottom

panel

-RRB-

Generating

T

by

means

of

an

alternative

cause

the

lever

does

not

predict

F

because

the

manipulation

of

an

effect

does

not

influence

its

cause

-LRB-

L

-RRB-

A

dissociation

between

seeing

and

doing

would

be

remarkable

because

in

the

observational

learning

phase

T

is

positively

correlated

with

L

The

only

theoretical

model

that

derives

correct

predictions

for

interventions

from

observational

learning

data

is

causal

Bayes

nets

-LRB-

-RRB-

Predictions

for

observations

make

use

of

the

full

causal

model

acquired

during

observational

learning

-LRB-

top

panel

-RRB-

Predictions

for

interventions

however

are

based

on

a

modified

graph

-LRB-

bottom

panel

-RRB-

the

insight

that

generating

T

in

the

common-cause

model

happens

independently

of

its

usual

cause

L

is

modeled

by

removing

the

causal

arrow

that

leads

into

the

manipulated

effect

a

manipulation

called

graph

surgery

-LRB-

-RRB-

Because

the

manipulated

T

is

unrelated

to

L

the

likelihood

of

other

effect

F

should

not

be

altered

by

presence

A

possible

alternative

associationist

explanation

of

the

failure

to

expect

F

after

an

intervention

in

T

may

be

that

the

animal

does

not

expect

F

because

it

lacks

prior

instrumental

learning

experiences

relating

lever

presses

to

F

This

alternative

theory

however

erroneously

also

predicts

a

failure

to

expect

F

in

the

presence

of

noise

-LRB-

N

-RRB-

after

these

events

had

been

paired

during

observational

learning

-LRB-

Fig.

-RRB-

Because

of

the

direct

causal

link

between

N

and

F

causal

Bayes

nets

predict

that

animals

should

equally

expect

F

regardless

of

whether

N

is

observed

or

generated

by

an

intervention

Recent

research

with

similar

tasks

has

shown

-LRB-

-RRB-

that

human

participants

are

capable

of

deriving

correct

predictions

for

interventions

on

the

basis

of

observational

data

-LRB-

-RRB-

In

experiment

rats

were

trained

on

the

causal

model

shown

in

Fig.

using

an

observational

Pavlovian

procedure

-LRB-

-RRB-

Training

consisted

of

three

types

of

trials

interspersed

within

each

session

The

first

type

of

trial

was

presentations

of

stimulus

L

-LRB-

a

flashing

light

or

click

train

-RRB-

forward-paired

with

Fig.

Common-cause

and

causal

chain

models

from

experiment

-LRB-

Left

-RRB-

Observed

causal

relations

-LRB-

Right

-RRB-

Model

modified

under

the

assumption

of

an

intervention

in

T.

stimulus

T

-LRB-

a

tone

or

noise

-RRB-

the

second

was

presentations

of

stimulus

L

forward-paired

with

stimulus

F

-LRB-

a

delivery

of

sucrose

solution

-RRB-

the

third

was

simultaneous

presentations

of

stimulus

N

-LRB-

a

noise

or

tone

-RRB-

and

s

of

F

We

trained

each

causal

link

in

the

common-cause

model

separately

to

make

it

more

likely

that

subjects

did

not

induce

a

direct

link

between

effects

T

and

F

Why

did

the

rats

not

induce

that

the

alternative

effect

is

always

absent

when

the

cause

and

one

effect

are

present

-LRB-

that

is

conditioned

inhibition

-RRB-

With

few

learning

trials

rats

tend

to

integrate

individual

learning

relations

into

a

coherent

integrated

model

Only

after

many

trials

do

rats

encode

the

explicit

absence

of

the

nonpresented

cues

-LRB-

-RRB-

Supporting

these

findings

the

results

of

all

our

experiments

show

that

rats

induced

second-order

excitatory

rather

than

inhibitory

relations

-LRB-

-RRB-

Apparently

in

the

initial

phases

of

learning

rats

tend

to

conservatively

treat

the

absent

but

expected

events

as

possibly

present

but

missed

A

similar

ability

to

combine

individually

learned

causal

links

into

complex

causal

models

has

been

demonstrated

in

humans

-LRB-

-RRB-

Do

rats

treat

L

as

a

common

cause

of

both

T

and

F

and

do

they

correctly

differentiate

between

seeing

and

doing

with

respect

to

T

and

N

Rats

were

allocated

to

one

of

four

test

conditions

and

were

placed

in

the

conditioning

chamber

with

a

lever

present

This

lever

had

not

been

present

in

the

observational

learning

phase

so

that

no

prior

instrumental

knowledge

was

available

Rats

in

condition

intervene-T

received

a

presentation

of

T

each

time

they

pressed

the

lever

Rats

in

condition

observe-T

merely

observed

presentations

of

T

independently

of

any

emitted

lever

presses

Conditions

intervene-N

and

observe-N

were

conducted

in

an

identical

fashion

except

that

N

was

either

the

product

of

an

intervention

by

lever

pressing

or

was

observed

We

recorded

the

number

of

nose

pokes

into

the

magazine

where

F

had

been

delivered

during

the

training

phase

to

assess

the

rats

expectation

of

F.

Causal

Bayes

nets

predict

that

observing

T

in

condition

observe-T

should

lead

the

rats

to

reason

that

the

temporally

prior

cause

L

was

probably

present

-LRB-

but

missed

-RRB-

and

to

consequently

expect

that

F

should

also

be

present

therefore

they

should

emit

many

nose

pokes

In

contrast

rats

in

condition

intervene-T

should

attribute

T

to

their

intervention

and

therefore

expect

L

and

consequently

its

effect

F

to

occur

with

the

probability

corresponding

to

the

base

rate

of

its

cause

L.

Consequently

we

should

observe

a

lower

rate

of

nose

poking

in

condition

intervene-T

than

in

condition

observe-T

There

should

not

be

any

difference

in

rates

of

nose

poking

however

between

conditions

intervene-N

and

observe-N

The

direct

causal

relationship

should

lead

the

rats

to

expect

F

regardless

of

whether

N

was

observed

or

intervened

on

at

test

Unlike

causal

Bayes

nets

associationist

theories

predict

equivalent

nose

poking

in

the

presence

of

T

in

both

the

observe

and

intervene

conditions

Figure

shows

the

mean

rate

of

nose

poking

per

presentation

of

stimuli

T

and

N

as

a

function

of

test

condition

-LRB-

with

a

maximum

rate

of

nose

pokes

per

presentation

-RRB-

As

predicted

by

causal

Bayes

nets

rats

that

produced

T

through

a

lever-press

intervention

-LRB-

condition

intervene-T

-RRB-

made

fewer

nose

pokes

than

rats

that

merely

observed

T

-LRB-

condition

observe-T

-RRB-

However

rats

that

intervened

in

N

-LRB-

condition

Intervene-N

-RRB-

which

was

trained

as

a

direct

predictor

of

F

did

not

nose

poke

less

than

rats

that

merely

observed

N

-LRB-

condition

observe-N

-RRB-

EAn

analysis

of

the

lever

press

data

ruled

out

selective

interference

between

lever

pressing

and

nose

poking

-LRB-

-RRB-

In

experiment

we

observed

a

dissociation

between

seeing

and

doing

within

the

commoncause

model

whereas

both

tasks

led

to

identical

expectations

with

the

direct

causal

link

which

is

consistent

with

causal

Bayes

nets

A

critic

might

point

out

that

we

found

a

dissociation

within

a

complex

causal

model

with

two

separately

learned

links

-LRB-

the

common-cause

model

-RRB-

whereas

we

found

similar

responses

to

the

less

complex

direct

link

To

rule

out

complexity

or

second-order

learning

as

the

basis

of

our

dissociation

we

compared

a

common-cause

condition

with

an

equally

complex

causal

chain

in

which

the

individual

causal

links

were

also

presented

separately

-LRB-

that

is

second-order

conditioning

-RRB-

-LRB-

Fig.

-RRB-

Whereas

causal

Bayes

nets

predict

a

dissociation

between

seeing

and

doing

in

the

common-cause

model

no

such

dissociation

is

expected

for

the

causal

chain

Regardless

of

whether

the

initial

cause

-LRB-

T

-RRB-

of

the

chain

is

observed

or

generated

by

means

of

an

intervention

the

intermediate

-LRB-

L

-RRB-

and

final

effect

-LRB-

F

-RRB-

should

equally

be

expected

In

experiment

rats

received

either

common-cause

training

as

in

experiment

or

causal-chain

training

which

was

identical

except

that

T

preceded

L

during

observational

learning

-LRB-

-RRB-

In

the

test

phase

groups

Common-Cause

model

Observing

L

Intervening

L

T

F

T

F

Causal

Chain

Observing

L

Intervening

L

T

F

T

F

SCIENCE

VOL

FEBRUARY

Downloaded

from

on

May

REPORTS

p

p

Fig.

Experiment

-LRB-

left

panels

-RRB-

Mean

nose

p

pokes

during

test

lus

T

-LRB-

top

panel

-RRB-

or

s

after

the

termination

of

T

-LRB-

bottom

panel

-RRB-

after

a

lever

press

-LRB-

intervene

P

in

both

panels

-RRB-

or

no

lever

press

-LRB-

serve

P

and

Common-Cause

Chain

in

top

and

bottom

panels

Chain

Unpaired

Training

respectively

-RRB-

Commoncause

and

chain

indicate

p

Intervene

the

type

of

causal

model

Observe

training

Bars

indicate

p

SEM

Planned

comparisons

p

from

two-way

ANOVAs

are

shown

Experiment

-LRB-

right

panels

-RRB-

Mean

nose

pokes

during

test

stimulus

T

-LRB-

top

panel

-RRB-

or

s

after

the

termination

of

T

-LRB-

bottom

panel

-RRB-

Common-Cause

Chain

after

a

lever

press

-LRB-

interChain

Unpaired

vene

-RRB-

or

no

lever

press

-LRB-

observe

-RRB-

Chain

and

unpaired

indicate

the

type

of

causal

model

training

Bars

indicate

SEM

P

and

in

top

and

bottom

panels

respectively

for

the

main

effect

of

training

Mean

nose

pokes

ing

mechanisms

but

they

are

consistent

with

causal

Bayes

net

theories

The

core

competency

of

reasoning

with

causal

models

seems

to

be

already

in

place

in

animals

even

when

elaborate

physical

knowledge

may

not

yet

be

available

References

and

Notes

D.

Hume

Treatise

on

Human

Nature

L.

A.

Selby-Bigge

Ed

-LRB-

Oxford

Univ.

Press

London

-RRB-

-LRB-

first

published

-RRB-

P.

Spirtes

C.

Glymour

R.

Scheines

Causation

Prediction

and

Search

-LRB-

Springer-Verlag

New

York

-RRB-

J.

Pearl

Causality

-LRB-

Cambridge

Univ.

Press

Cambridge

-RRB-

J.

Woodward

Making

Things

Happen

-LRB-

Oxford

Univ.

Press

Oxford

-RRB-

A.

Dickinson

D.

Shanks

Causal

Cognition

D.

Sperber

A.

J.

Premack

Eds

-LRB-

Clarendon

Press

Oxford

-RRB-

pp.

A.

Gopnik

et

al.

Psychol

Rev.

-LRB-

-RRB-

A.

Gopnik

L.

Schulz

Trends

Cogn

Sci

-LRB-

-RRB-

M.

R.

Waldmann

J.

Exp

Psychol

Learn

Mem

Cogn

-LRB-

-RRB-

M.

Tomasello

J.

Call

Primate

Cognition

-LRB-

Oxford

Univ.

Press

London

-RRB-

D.

J.

Povinelli

Folk

Physics

for

Apes

-LRB-

Oxford

Univ.

Press

New

York

-RRB-

E.

Visalberghi

L.

Limongelli

J.

Comp

Psychol

-LRB-

-RRB-

N.

J.

Mulcahy

J.

Call

R.

I.

M.

Dunbar

J.

Comp

Psychol

-LRB-

-RRB-

L.

R.

Santos

C.

T.

Miller

M.

D.

Hauser

Anim

Cogn

-LRB-

-RRB-

M.

R.

Waldmann

Y.

Hagmayer

J.

Exp

Psychol

Learn

Mem

Cogn

-LRB-

-RRB-

A.

Dickinson

B.

Balleine

in

Steven

's

Handbook

of

Experimental

Psychology

vol

H.

Pashler

R.

Gallistel

Eds

-LRB-

Wiley

New

York

ed

-RRB-

pp.

S.

A.

Sloman

D.

A.

Lagnado

Cogn

Sci

-LRB-

-RRB-

Materials

methods

and

procedural

details

are

available

as

supporting

material

on

Science

Online

H.

Yin

R.

C.

Barnet

R.

R.

Miller

J.

Exp

Psychol

Anim

Behav

Process

-LRB-

-RRB-

H.

I.

Savastano

R.

R.

Miller

Behav

Process

-LRB-

-RRB-

J.

C.

Perales

A.

Catena

A.

Maldonado

Learn

Motiv

-LRB-

-RRB-

We

thank

T.

Dickinson

K.

Holyoak

K.

Leising

W.

D.

Stahlman

T.

Beckers

and

D.

Penn

for

helpful

comments

This

research

was

supported

by

National

Institute

of

Mental

Health

grant

-LRB-

A.P.B.

-RRB-

and

by

the

Japan

Ministry

of

Science

-LRB-

K.S.

-RRB-

common-cause

intervene

and

chain-intervene

received

presentations

of

T

each

time

the

lever

was

pressed

Groups

common-cause

observe

and

chain-observe

merely

observed

T

We

report

the

number

of

nose

pokes

during

the

presentation

of

T

and

during

the

period

beginning

s

after

the

termination

of

T

-LRB-

post-T

interval

-RRB-

for

all

subjects

In

the

chain

condition

F

should

rationally

be

expected

between

and

s

after

delivery

of

T

-LRB-

-RRB-

In

contrast

the

expected

time

of

delivery

of

F

for

rats

that

received

common-cause

training

is

during

T

itself

Figure

shows

the

mean

rate

of

nose

poking

on

test

trials

with

T.

Group

commoncause

intervene

nose

poked

less

than

group

common-cause

observe

which

replicates

the

pattern

of

experiment

In

contrast

no

difference

was

found

between

groups

chain-intervene

and

chain-observe

as

predicted

by

causal

Bayes

nets

Rats

in

group

chain-intervene

did

not

nose

poke

more

than

did

rats

in

group

commoncause

intervene

This

low

level

of

responding

does

not

reflect

a

failure

to

learn

a

causal

chain

however

Experiment

replicated

the

chain

condition

and

added

groups

for

which

T

and

L

were

unpaired

during

observational

learning

-LRB-

-RRB-

Figure

reveals

no

difference

between

seeing

and

doing

as

predicted

by

causal

Bayes

nets

Moreover

responding

in

the

causal-chain

groups

was

higher

than

in

the

unpaired

groups

which

signifies

that

the

rats

had

indeed

learned

the

second-order

chain

relations

A

number

of

researchers

have

recently

concluded

that

causal

reasoning

is

a

faculty

that

divides

humans

from

animals

-LRB-

-RRB-

The

present

results

cast

doubt

on

that

conclusion

With

tasks

that

did

not

require

complex

physical

knowledge

the

experiments

have

shown

that

rats

grasp

the

relationship

between

seeing

and

doing

Rats

made

correct

inferences

for

instrumental

actions

on

the

basis

of

purely

observational

learning

and

they

correctly

differentiated

between

common-cause

models

causal

chains

and

direct

causal

links

These

results

contradict

the

view

that

causal

learning

in

rats

is

solely

driven

by

associative

learn

Supporting

Online

Material

Materials

and

Methods

References

October

accepted

January

science

FEBRUARY

VOL

SCIENCE

Downloaded

from

on

May

CORRECTIONS

CLARIFICATIONS

ERRATUM

Post

date

October

Reports

Causal

reasoning

in

rats

by

A.

P.

Blaisdell

et

al.

-LRB-

Feb.

p.

-RRB-

The

wrong

input

data

were

used

to

generate

Fig.

The

corrected

figure

is

shown

here

The

error

does

not

change

the

conclusions

of

the

paper

Fig.

SCIENCE

ERRATUM

POST

DATE

OCTOBER

Downloaded

from

on

May

CORRECTIONS

CLARIFICATIONS

ERRATUM

Post

date

August

SCIENCE

ERRATUM

POST

DATE

AUGUST

Downloaded

from

on

May

Reports

Causal

reasoning

in

rats

by

A.

P.

Blaisdell

et

al.

-LRB-

February

p.

-RRB-

There

are

three

minor

typos

in

the

Supporting

Online

Materials

First

test

sessions

for

Experiments

and

were

minutes

Second

the

number

of

background

nose

pokes

in

Experiment

were

-LRB-

Conditions

Intervene-T

-RRB-

-LRB-

Condition

Observe-T

-RRB-

-LRB-

Condition

Intervene-N

-RRB-

and

-LRB-

Condition

Observe-N

-RRB-

Third

in

Experiment

the

F

value

for

the

planned

comparison

between

condition

Intervene-T

versus

Observe-T

was

p

A

reanalysis

of

the

data

from

Experiment

revealed

that

the

test-trial

data

for

one

subject

from

group

Unpaired-Observe

was

inadvertently

counted

twice

in

the

statistical

analyses

A

reanalysis

on

the

corrected

data

results

in

a

change

of

three

F

values

by

a

tenth

of

a

point

or

less

and

thus

has

no

effect

on

the

outcome

of

the

analyses

The

authors

failed

to

revise

the

caption

in

the

corrected

Fig.

-LSB-

Science

-LRB-

-RRB-

-RSB-

The

F

values

were

slightly

different

than

those

reported

Corrected

values

show

the

main

effect

of

inference

type

F

-LRB-

-RRB-

P

and

the

interaction

F

-LRB-

-RRB-

P

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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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NEWS FEATURE NATURE | Vol 441 | 25 May 2006 2006 Nature Publishing Group NATURE | Vol 441 | 25 May 2006 C. DARKIN NEWS FEATURE WHAT IS A GENE ? The idea of genes as beads on a DNA string is fast fading . Protein-coding sequences have no clear beginning or end and RNA is a key part of the information package , reports Helen Pearson . ene ' is not a typical four-letter word . It is not offensive . It is never bleeped out of TV shows . And where the meaning of most fourletter words is all too clear , that of gene is not . The more expert scientists become in molecular genetics , the less easy it is to be sure about what , if anything , a gene actually is . Rick Young , a geneticist at the Whitehead Institute in Cambridge , Massachusetts , says that when he first started teaching as a young professor two decades ago , it took him about two hours to teach fresh-faced undergraduates what a gene was and the nuts and bolts of how it worked . Today , he and his colleagues need three months of lectures to convey the concept of the gene , and that 's not because the students are any less bright . `` It takes a whole semester to teach this stuff to talented graduates , '' Young says . `` It used to be we could give a one-off definition and now it 's much more complicated . '' In classical genetics , a gene was an abstract concept -- a unit of inheritance that ferried a characteristic from parent to child . As biochemistry came into its own , those characteristics were associated with enzymes or proteins , one for each gene . And with the advent of molecular biology , genes became real , physical things -- sequences of DNA which when converted into strands of so-called messenger RNA could be used as the basis for building their associated protein piece by piece . The great coiled DNA molecules of the chromosomes were seen as long strings on which gene sequences sat like discrete beads . This picture is still the working model for many scientists . But those at the forefront of genetic research see it as increasingly old-fashioned -- a crude approximation that , at best , hides fascinating new complexities and , at worst , blinds its users to useful new paths of enquiry . Information , it seems , is parceled out along chromosomes in a much more complex way than was originally supposed . RNA molecules are not just passive conduits through which the gene 's message flows into the world but active regulators of cellular processes . In some cases , RNA may even pass information across generations -- normally the sole preserve of DNA . An eye-opening study last year raised the possibility that plants sometimes rewrite their DNA on the basis of RNA messages inherited from generations past1 . A study on page 469 of this issue suggests that a comparable phenomenon might occur in mice , and by implication in other mammals 2 . If this type of phenomenon is indeed widespread , it `` would have huge implications , '' says evolutionary geneticist ` G Laurence Hurst at the University of Bath , UK . viously unimagined scope of RNA . The one gene , one protein idea is coming `` All of that information seriously challenges our conventional definition of a gene , '' says under particular assault from researchers who molecular biologist Bing Ren at the University are comprehensively extracting and analysing of California , San Diego . And the information the RNA messages , or transcripts , manufacchallenge is about to get even tougher . Later tured by genomes , including the human and this year , a glut of data will be released from mouse genome . Researchers led by Thomas the international Encyclopedia of DNA Ele - Gingeras at the company Affymetrix in Santa ments -LRB- ENCODE -RRB- project . The pilot phase of Clara , California , for example , recently studied ENCODE involves scrutinizing roughly 1 % of all the transcripts from ten chromosomes the human genome in unprecedented detail ; across eight human cell lines and worked out precisely where on the chrothe aim is to find all the `` We 've come to the mosomes each of the transequences that serve a useful scripts came from3 . purpose and explain what realization that the that purpose is . `` When we The picture these studies genome is full of started the ENCODE project paint is one of I had a different view of overlapping transcripts . '' mind-boggling complexity . what a gene was , '' says conInstead of discrete genes -- Phillip Kapranov tributing researcher Roderic dutifully mass-producing Guigo at the Center for Genomic Regulation identical RNA transcripts , a teeming mass of in Barcelona . `` The degree of complexity we 've transcription converts many segments of the seen was not anticipated . '' genome into multiple RNA ribbons of differing lengths . These ribbons can be generated from both strands of DNA , rather than from just one Under fire The first of the complexities to challenge molec - as was conventionally thought . Some of these ular biology 's paradigm of a single DNA transcripts come from regions of DNA previsequence encoding a single protein was alterna - ously identified as holding protein-coding tive splicing , discovered in viruses in 1977 -LRB- see genes . But many do not . `` It 's somewhat revolu`Hard to track ' , overleaf -RRB- . Most of the DNA tionary , '' says Gingeras 's colleague Phillip sequences describing proteins in humans have a Kapranov . `` We 've come to the realization that modular arrangement in which exons , which the genome is full of overlapping transcripts . '' Other studies , one by Guigo 's team4 , and one carry the instructions for making proteins , are interspersed with non-coding introns . In alter - by geneticist Rotem Sorek5 , now at Tel Aviv native splicing , the cell snips out introns and University , Israel , and his colleagues , have sews together the exons in various different hinted at the reasons behind the mass of tranorders , creating messages that can code for dif - scription . The two teams investigated occaferent proteins . Over the years geneticists have sional reports that transcription can start at a also documented overlapping genes , genes DNA sequence associated with one protein within genes and countless other weird arrange - and run straight through into the gene for a ments -LRB- see ` Muddling over genes ' , overleaf -RRB- . completely different protein , producing a Alternative splicing , however , did not in itself fused transcript . By delving into databases of require a drastic reappraisal of the notion of a human RNA transcripts , Guigo 's team estigene ; it just showed that some DNA sequences mate that 4 5 % of the DNA in regions concould describe more than one protein . Today 's ventionally recognized as genes is transcribed assault on the gene concept is more far reach - in this way . Producing fused transcripts could ing , fuelled largely by studies that show the pre - be one way for a cell to generate a greater variety of proteins from a limited number of exons , the researchers say . Many scientists are now starting to think that the descriptions of proteins encoded in DNA know no borders -- that each sequence reaches into the next and beyond . This idea will be one of the central points to emerge from the ENCODE project when its results are published later this year . Kapranov and others say that they have documented many examples of transcripts in which protein-coding exons from one part of Spools of DNA -LRB- above -RRB- still harbour surprises , with the genome combine with exons from another one protein-coding gene often overlapping the next . 2006 Nature Publishing Group 399 P. PLAILLY/SPL NEWS FEATURE NATURE | Vol 441 | 25 May 2006 Hard to track ABBEY OF ST THOMAS , BRNO , CZECH REPUBLIC 1860s After playing with pea plants , Austrian monk Gregor Mendel defines the basic rules of inheritance . Traits are determined by discrete units that are passed from one generation to the next . 1909 Danish botanist Wilhelm Johanssen coins the word ` gene ' for the unit associated with an inherited trait , although the physical basis remains unknown . 1910 Thomas Morgan 's work on fruitflies -LRB- right -RRB- , shows that genes sit on chromosomes , leading to the idea of genes as beads on a string . 1941 George Beadle and Edward Tatum introduce the concept that one gene makes one enzyme . 1944 Genes are made of DNA , find Oswald Avery -LRB- below -RRB- , Colin MacLeod and Maclyn McCarty . 1953 James Watson and Francis Crick publish the chemical structure of DNA ; the central dogma of molecular biology emerges in which information flows from DNA to RNA to protein . 1977 Richard Roberts and Phillip Sharp discover that genes can be split into segments , leading to the idea that one gene can make several proteins . 1993 The first microRNA is identified in the worm Caenorhabditis elegans . 2003 GeneSweep : Human geneticists come up with a definition for protein-coding genes in order to decide on a winner for a bet on the number of human genes . The winner is announced , but geneticists acknowledge that they do n't know the true answer . 2006 The idea that human genes are one long continuum begins to emerge . 400 when scientists have searched for the genetic basis of a disease or other characteristic they have overwhelmingly found the underlying mutation to be in a protein-coding gene rather than in another region . `` The preponderance of evidence suggests that proteincoding genes will hold their own when the day is over , '' Hogenesh says . Some of the recent discoveries -- that the human genome makes a continuum of transcripts and that cells produce masses of noncoding RNA molecules -- have not posed much of a problem to people outside the world of molecular biology . Population Slippery concept The large transcriptional surveys suggest geneticists can examine how a trait is passed that a vast amount of the RNA manufactured down and evolves regardless of the precise by the mouse and human genomes do not molecular mechanism that underlies it . For code for proteins . Last year a consortium of example , geneticists can build models showresearchers in Japan , for example , estimated ing how a mutation is inherited whether it that a whopping 63 % of the mouse genome is affects a protein , a non-coding RNA or a regtranscribed7 ,8 ; only 1 2 % of the genome is ulatory region . `` I do n't actually care if it 's thought to be spanned by sequences that making a protein or not , '' says Hurst . `` The equations are still the same . '' contain everyday exons . But the same ca n't be said for studies The discovery of RNA sequences that are n't just intermediates between the DNA revealing so-called extragenomic modes of and the protein-making machinery is not inheritance . In recent years , many investiganew in itself ; the cell 's protein-building appa - tors have focused on epigenetic inheritance , ratus requires a number of RNA molecules in which information is passed from parent as well as proteins to operate . But the finding to offspring independent of the DNA of ` microRNAs ' and other RNA molecules sequence . And this week in Nature -LRB- see page now known to be vital in controlling many 469 -RRB- , Minoo Rassoulzadegan 's team at the cellular processes in plants and animals , and French National Institute for Health and the newly revealed ferment of RNA tran - Medical Research -LRB- INSERM -RRB- in Nice , France , scription , contributes to the view that RNA reports that RNA may sometimes be compliactively processes and carries out the cating traditional models of inheritance . In mice , mutations in the Kit gene cause instructions in the genome . Perhaps the regions that make non-coding white patches on the tail and feet ; if a mouse RNA should also carry the status of genes , if has one normal Kit gene and one mutated not the name itself . `` I think it 's time for peo - one it will have the spots . The odd thing is that some of the offspring of ple to take a deep breath `` A lot of the such mice , who inherit two and step back , '' says molecinformation is being normal Kit genes , still have the ular biologist John Mattick of the University of transacted by RNA . '' white tail . The French group suggest that the mutant Kit Queensland in Brisbane , -- John Mattick gene manufactures abnormal Australia . `` A lot of the information in the system is being transacted RNA molecules , which accumulate in sperm and pass into the egg . These bits of RNA by RNA . '' Although functions have been identified somehow silence the normal Kit gene in the for several RNA molecules , the crux of the next generation and subsequent ones , prodebate now is the extent to which all the ducing the spotted-tail effect . `` We are conextra RNA plays a part . It is conceivable that vinced that it 's a more general phenomenon , '' it is easier to overtranscribe and ignore the says co-author Franc ois Cuzin . If this is strange , the work reported last rubbish than to invest in systems that produce only what is needed . A study from last year1 on the cress plant Arabidopsis by year , however , hints that at least some of the Robert Pruitt and his colleagues at Purdue mass of RNAs is doing something useful . University in West Lafayette , Indiana , is even Working at the Genomics Institute of the stranger . Here the gene involved is called Novartis Research Foundation in San Diego , HOTHEAD . Pruitt and his co-workers ' California , John Hogenesch and his co - analysis shows that some plants do not carry workers systematically quenched the activity the mutant version of HOTHEAD that their of more than 500 non-coding RNAs in parents possessed . These plants had replaced human cells and found that eight were the abnormal DNA sequence with the reguinvolved in cell signalling and growth9 . lar code possessed by earlier generations . But Hogenesh , and many other scientists , `` It 's like , whoa , this changes everything , '' remain convinced that non-coding RNAs Pruitt says . `` It definitely changes my view of are much less important , functionally , than inheritance . '' Pruitt is now working to explain how the those that describe proteins ; in the past , part that can be hundreds of thousands of bases away , with several other ` genes ' in between . This continuum of genes might even spill over the boundaries of chromosomes : last year , Richard Flavell at Yale University School of Medicine in New Haven , Connecticut , documented human immunesystem genes that seem to be controlled by regulatory regions from another chromosome6 . `` Discrete genes are starting to vanish , '' Guigo says . `` We have a continuum of transcripts . '' 2006 Nature Publishing Group TENNESSEE STATE LIB . & ARCHIVES/NLM/NIH GRAPHIC SCIENCE/ALAMY NATURE | Vol 441 | 25 May 2006 NEWS FEATURE M. ALICE WEBB Back-up copies : mutant DNA in the cress plant may be ` corrected ' by inherited RNA . plant could perform such a feat . One idea is that they carry a back-up copy of their grandparents ' genetic information encoded in RNA that is passed into seeds along with the regular DNA and is then used as a template to ` correct ' certain genes . Conceivably , Pruitt says , some of the mystery non-coding transcripts could be responsible . `` I think there 's something being inherited outside what we think of as the conventional DNA genome . '' Changing views The implications of such findings for our understanding of evolution have yet to be figured out . But research into the role of RNA as a carrier of information across generations promises to enrich -- and complicate -- the notion of a gene yet further . Leaving aside the can of worms that studies on epigenetics are beginning to open up , does it matter that many scientists not directly concerned with molecular mechanisms continue to think of genetics in simpler terms ? Some geneticists say yes . They worry that researchers working with an oversimplistic idea of the gene could discard important results that do n't fit . A medical researcher , for example , might gloss over the many different transcripts generated by a sequence at one location . And the lack of a clear idea of what a gene is might also hinder collaboration . `` I find it sometimes very difficult to tell what some - Muddling over genes Science philosophers Karola Stotz , at Indiana University in Bloomington , and Paul Griffiths , now at the University of Queensland in Australia , are attempting to measure the extent of working biologists ' bewilderment over genes . They collected together 14 weird and wonderful -LRB- but real -RRB- genetic arrangements and asked biologists to decide whether each represents one , or more than one , gene . One is a DNA segment that uses some of the same protein-coding sequences to manufacture two entirely different proteins with distinct functions . In another , one ` gene ' is nestled within the non-protein coding intron of another . Another protein is assembled when four different RNA molecules , made from DNA scattered over 40,000 base pairs , are assembled into one transcript . Confused ? So were the 500 biologists who completed the questionnaire . Stotz and Griffiths found that 60 % are typically sure of one answer , and 40 % are confident of another . Hardly any confess that they do n't know . Stotz wants to examine whether scientists working in separate disciplines tend to view the situations in different lights . `` It will be interesting to know if there is some order to the confusion , '' Stotz says . H.P. one means when they talk about genes because we do n't share the same definition , '' says developmental geneticist William Gelbert of Harvard University in Cambridge , Massachusetts . Without a clear definition of a gene , life is also difficult for bioinformaticians who want to use computer programs to spot landmark sequences in DNA that signal where one gene ends and the next begins . But reaching a consensus over the definition is virtually impossible , as Karen Eilbeck can attest . Eilbeck , who works at the University of California in Berkeley , is a coordinator of the Sequence Ontology consortium . This defines labels for landmarks within genetic-sequence databases of organisms , such as the mouse and fly , so that the databases can be more easily compared . The consortium tries , for example , to decide whether a protein-coding sequence should always include the triplet of DNA bases that mark its end . Eilbeck says that it took 25 scientists the better part of two days to reach a definition of a gene that they could all work with . `` We had several meetings that went on for hours and everyone screamed at each other , '' she says . The group finally settled on a loose definition that could accommodate everyone 's demands . -LRB- Since you ask : `` A locatable region of genomic sequence , corresponding to a unit of inheritance , which is associated with regulatory regions , transcribed regions and/or other functional sequence regions . '' -RRB- Rather than striving to reach a single definition -- and coming to blows in the process -- most geneticists are instead incorporating less ambiguous words into their vocabulary such as transcripts and exons . When it is used , the word ` gene ' is frequently preceded by ` proteincoding ' or another descriptor . `` We almost have to add an adjective every time we use that noun , '' says Francis Collins , director of the National Human Genome Research Institute at the National Institutes of Health in Bethesda , Maryland . But however much geneticists struggle to pin down the elusive gene , it is precisely its ambiguous nature that fuels their continued curiosity . `` It 's ever more fascinating , '' says Whitehead 's Young . Some things , it seems , are not best portrayed by a crude four-letter word . Helen Pearson is a reporter working for Nature in New York . 1 . Lolle , S. J. , Victor , J. L. , Young , J. M. & Pruitt . R. E. Nature 434 , 505 509 -LRB- 2005 -RRB- . 2 . Rassoulzadegan , M. et al. . Nature 441 , 469 474 -LRB- 2006 -RRB- . 3 . Cheng J. et al. . Science 308 , 1149 1154 -LRB- 2005 -RRB- . 4 . Parra , G. et al. . Genome Res . 16 , 37 44 -LRB- 2006 -RRB- . 5 . Akiva , P. et al. . Genome Res . 16 , 30 36 -LRB- 2006 -RRB- . 6 . Spilianakis , C. G. , Lalioti , M. D. , Town , T. , Lee , G. R. & Flavell , R. A. Nature 435 , 637 645 -LRB- 2005 -RRB- . 7 . FANTOM Consortium and RIKEN Genome Exploration Research Group and Genome Science Group -LRB- Genome Network Project Core Group -RRB- Science 309 , 1559 1563 -LRB- 2005 -RRB- . 8 . RIKEN Genome Exploration Research Group and Genome Science Group -LRB- Genome Network Project Core Group -RRB- and the FANTOM Consortium Science 309 , 1564 1566 -LRB- 2005 -RRB- . 9 . Willingham , A. T. et al. . Science 309 , 1570 1573 -LRB- 2005 -RRB- . 2006 Nature Publishing Group 401

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