Modelling Evolution of Regulatory Networks in Artificial Bacteria

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Yolanda Sanchez-Dehesa, David P. Parsons, Jose Maria Pena, Guillaume Beslon

To cite this version:

Yolanda Sanchez-Dehesa, David P. Parsons, Jose Maria Pena, Guillaume Beslon. Modelling Evolution of Regulatory Networks in Artificial Bacteria. Mathematical Modelling of Natural Phenomena, EDP Sciences, 2008, 2, 3, pp.27-66. �10.1051/mmnp:2008054�. �hal-01500393�

Modelling Evolution of Regulatory Networks

in Artiial Bateria

Y. Sanhez-Dehesa

a,c

, D. Parsons

a

, J.M. Peña

b

, and G. Beslon 1,a,c

a

LIRIS CNRS UMR5205, INSA-Lyon, Université de Lyon, 69621 Villeurbanne,Frane

b

DATSI, UniversidadPoliténiade Madrid, 28660 Madrid,Spain

c

Institut Rhne-Alpindes Systèmes Complexes (IXXI), Lyon, Frane

Abstrat. Studying the evolutive and adaptative mehanisms of prokaryotes is a ompli-

atedtask. Asthesemehanismsannotbeeasilystudiedinvivo,itisneessarytoonsider

other methods. We have therefore developed the RAevol model, a modeldesigned to study

the evolutionof bateriaand theiradaptationtothe environment. Our modelsimulates the

evolution of apopulationof artiialbateriaina hangingenvironment, providingus with

aninsight intothe strategies that digitalorganismsdevelop toadapt tonew onditions.

In this paper we desribe the priniples and arhiteture of the model, fousing on the

mehanismsof the regulatorynetworksof artiialorganisms. Experiments were onduted

onpopulations of artiialbateriaunder onditions of stress. We study the ways in whih

organismsadapttoenvironmentalhanges andexamine thestrategies they adopt. Ananal-

ysisoftheseadaptationstrategiesispresented andabriefoverviewwasproposedonerning

the patterns and topologialharateristis of the evolved regulatory networks.

Key words: evolution,regulatory networks, modelling, motifs,adaptationmehanisms

AMS subjet lassiation: 9204, 92D10, 92D15

1 Introdution

Prokaryote organisms are very diverse, livingindierentenvironments and developingvari-

ous abilities. Bateria are found in every eosystem some being olonized only by miro-

organisms illustratingthe impressive adaptation apabilities of prokaryotes. They an be

1

Correspondingauthor. Email: guillaume.beslonliris.nrs.fr

Article available at http://www.mmnp-journal.org or http://dx.doi.org/10.1051/mmnp:2008054

ganisms(e.g., Buhnera aphidiola,whihlivesinsymbiosis withaphids,providingessential

amino aids for their host), or even in the human intestine where Esherihia oli favors

digestionand absorption of nutrients.

Bateria are goodexamplesof organismadaptation. They areable toreattovariations

in their environment at dierent levels: bateria strains an adapt to major environmental

hangesbyadarwinianevolutionaryproessandindividualbateriaanadapttoshort-term

hangesintheir environment. Toahievethis kindofadaptation atdierentlevels, bateria

havedeveloped a largerepertoireof strategies that may themselvesbeoptimized depending

onthe harateristisofthe environment: stability,periodiity,stohastiity,ompetition...

Although a lot of dierent strategies (e.g. evolution, regulation, bet-hedging, adaptive

mutation, gene ampliation, Baldwin eet) have been identied and are relatively well

haraterized individually, we only have a very partial insight into how they ombine with

one another: in an idealizedenvironment, one an identify the optimal strategy and math-

ematially nd the optimal parameters. However real environments are far from ideal and

there will generally be a wide range of viable adaptation strategies, ombining e.g., regu-

lation and evolution, evolution and bet-hedging, regulation and gene ampliation or any

ombination of these. For instane, if the environment hanges slowly, bateria may have

enoughtimetomutateanddarwinianevolutionan besuienttoadapttonewonditions.

But,they maynot beabletoonserve omplexregulationstrategiessine mutationsquikly

degrade regulationmehanismswhen theseare inative[14℄. Now, if the environmentvaries

alittlefaster, evolution an beless eient than regulation,provided that bateriaare able

tosensetheirenvironmentatanaeptableost andthat environmentalhangesshowsome

regularities (e.g., swithes between two dierent energy soures as in the well-known la

operon). Onthe ontrary, rarebut unpreditable eventsput organismsunderstress and are

known to promote spei adaptive strategies suh as the development of mutator strains

[44℄. All these dierent strategies imply plastiity at dierent levels: geneti, metaboli,

physiologi, phenotypi,all of whih are involved inomplex interations.

Theseadaptationmehanismshelpbateriatoadapttohangingenvironments. However

eah has its own tempo, ranging fromslow (i.e., darwinian strategy) to fast (i.e. stohasti

perturbationsleading tophenotypivariability). In themiddle, genetiregulationenablesa

fast dynamiadaptation, enablingells toreat tohemialsignals. Regulationis the main

mehanismtoprovideadaptivebehavioratametabolilevel. However, regulationneverats

alone,itisobviouslyombinedwithevolution: genetivariations,genedupliation,geneloss

orhromosomalalterations[19℄onstituteavast repertoireofvariationsthatanbeusedby

abaterialstraintoadapttoitsenvironment,butthat analsoprovidebateriaindividuals

withtoolstodevelopmoreomplexadaptationmehanisms. Inspei onditionsevolution

givesrisetoregulatorysystemsthatenablefastadaptationtorapidlyhangingenvironments.

Intheaseofthelaoperon,regulationenablestheorganismtosaveenergywhenseveralfood

soures are available. It is supposed that regulation is a result of adaptation to hanging

environments. Yet, it an be shown that suh a system an be very sensitive to hanges

in the environment onditions: Dekel [14℄ has shown that only a few hundred generations

are neessary for E. oli to drastially hange its la operon behavior when plaed in new

onditions. At the other end of the time sale, the laoperon is known to have a stohasti

behavior [11, 17℄ and it an be shown that stohastiity of transription interats with the

regulatory ativity of the operon, delaying the operon swith [23℄. Thus, while regulation

ativity has long been supposed to be independent of slow evolutionary hanges or fast

stohasti variations, it is beoming more and more lear that the interations of all these

adaptation strategies must bestudied tofully understand their behavior [22℄.

Itisstillamatterofdebateinwhatkindofsituation/environmentevolutionpromotesthe

emergeneofregulatoryproessesandhowregulationinteratswiththeevolutionaryproess

itself. Hypotheses annot be easily studied on real living systems. Although experimental

evolution is possible with miro-organisms [16℄, traking hanges in genomes, regulatory

networks and even phenotypes is almost impossible in in vivo tests. An alternative is to

usedigitalorganismstostudythe genetibasesofadaptationinsilio [2℄. Insuhartiial

models,organisms(i.e.,omputationaldatastrutures)areplaedinasynthetienvironment

that provides them with resoures. In this environment the organisms reprodue, mutate

and ompete for the resoures, thus resulting in darwinian evolution. Sine the organisms

aswellasthe environmentare artiiallydened they anbothbeperfetlyand ompletely

desribed [38℄. Suh models have already shown their usefulness in studying evolution of

robustness[47℄ orinidentifyingindiretmutationalpressurethatregulates genomesize [29℄.

Yet, sine most of these models fous on mutational adaptation, they annot be used to

study omplex interations between the dierent adaptationmehanisms.

The denition of a suitablemodelto desribe this biologialproess would be useful to

takle many open questions in the literature of this domain: How do organisms adapt to

environmentalhanges? What isthe originof regulatorynetworks? Why doregulatorynet-

works appear during evolution? Howdo networks evolve over time? Studyingthe inlusion

of new nodes in already existing regulatory networks and studying the development of new

regulatory networks ould help to answer some of these questions and provide us with a

better understanding of network evolution.

Genetinetworksappeartobehighlyorganized: theyare modular[21℄,sale-free[7℄and

some motifs are overrepresented [4℄. Yet, the preise origin of these strutures is not fully

understood. In partiular, it is quite diult to distinguish between seletive origin (the

struture of the network is seleted beause it ensures a orret funtion in the organism's

environment), mutational origin (the mutational proess tends tofavor some strutures, as

inthepreferentialattahmentmodel[7℄)and indiretseletiveorigin(thenetwork struture

is seleted beause it is robust to mutation or, on the opposite, highly adaptable). It has

been shown that in some spei onditions, modular strutures an be seleted inevolved

networks [20, 25℄. Here again, modelling isan essentialtoolto takle suh questions.

Struture and dynamis of regulatorynetworks are atthe heartof systems biology. The

rapid development of this eld has been followed by the development of a very ative mod-

ellingativity ofsuhnetworks. Asfar asevolutionof regulatorynetworksisonerned, the

workhasbeenfousedonthequestionoftopologyevolution[25,26,49℄,evolutionofnetwork

robustness [3,12,42℄and evolutionof artiialfuntions[5,6,18, 32℄. Mostofthese papers

deal with diret evolution of geneti networks (i.e., in the model the network struture is

diretlymodiedbythegenetioperatorsmutations,rossing-overandrearrangements)or

seletionoftheindividualsonthe basisof thenetworkproperties(e.g.,seletionofaspei

topologyor seletionof a spei regulationdynami).

Additionally, many studies have been onduted to understand evolution of regulatory

networks from a bioinformati perspetive. Phylogeneti studies and sequene omparison

provide a quite preise view of the fores that shape bateria genomes and inuenes the

evolution of their regulatory networks [35℄. Thanks to these studies, it is now learer that

largegenomieventssuhasgenomirearrangement,horizontalgenetransfer(HGT)[19,31℄

or gene dupliation play a key role in the evolution of networks [45℄ and that the topology

of the network is fora large part indiretly shaped by the mutational dynami[13℄.

All these approahes fous on a spei fore that shape the network topology (e.g.,

mutational dynami, seletion for funtion, seletion for robustness - either mutational or

funtional robustness, ...). However, in a real biologialregulation network, allthese fores

are atwork simultaneouslyand thenetworktopologyresultsfromaompromisebetweenall

the onstraintsa networkand anorganism must fae. These onstraintsthemselves depend

ontheenvironmentalproperties: inastati environment,seletionforfuntionalrobustness

isimportantwhileinarandomly(butslowly)evolvingenvironment,themutationaldynami

and/or evolvability property may be ruial for the organism. Thus, to better understand

howthe environmentmodulatesthe emergene of spei network properties,anintegrated

modelisneededinwhihtheappearaneofdierentnetworktopologiesduringtheevolution

depends on the dynamial properties of the environment. Moreover, this model should

respet the main lines of organisms' evolution. Organisms should own a geneti sequene

that allows alarge variety of mutationalevents, aomplexgenotype-to-phenotype mapping

that inludes a proteome level and enables the evolution of a geneti network inside the

organism. Thus, it should be stratied from a genomi level (the sequene being diretly

modiedbymutationaleventswhileallotherorganizationlevelsareonlyindiretlymodied

depending on the eet of the random mutations) to a phenotype level (the phenotype

level being the only one subjet to seletion while the other organization levels are only

indiretly seleted depending on their inuene on the phenotype). The proteome level

must respet the ore properties of regulatory networks' evolution: the regulation network

isneitherdiretlymutatednor diretlyseleted. The nodes ofthe networks are theproteins

of the organism but the links result from a omplex interation between the organisms

proteinsand itsgenomisequene: eahprotein mayor may not interatwith the sequene

atspei loations,modifyingthetransriptionalativity ofapromoter and,onsequently,

the transription rate of one or many genes. Eah gene is then transribed at a spei

rate that depends on the intrinsi properties of its promoter and on the inuene of the

regulation network (inluding ativation, inhibition and self-regulation - see below). The

proteinonentrationis thengoverned bythe transriptionrate and by adegradationterm.

Moreover, the whole transription/translation proess is highly stohasti and it is now

reognized that stohastiity inuenes the fateof organisms[17℄.

Followingthesepriniples,wehavedevelopedtheRegulatoryArtiialEvolution model

(RAevol). In thismodel, artiialdigital bateriaevolveina variableenvironment. Along

theirevolution,thesebateriaaquiregenesandevolveaomplexgenome,aomplexregula-

tionnetworkandanadapted phenotype. Onanevolutionarytimesale, thebestindividuals

are those whih evolve the best mehanisms to fae environmental variations. We are then

able tounderstand whih ofthese mehanismsare eient dependingonthe environmental

onditions. Inthis paper, we rst desribethe generalprinipleof regulationin prokaryotes

and we expose the mehanisms that onstitute the ore of our model(Setion 2). Then we

preisely desribe the RAevol model (Setion 3), fousing on the regulation properties. Fi-

nallywe presenta simpleartiialevolution experimentthat illustratesthe mainproperties

of the model(Setion 4)and disuss evolutionary senariithat may be testedwith RAevol.

2 Priniples of Geneti Regulation in Prokaryotes

Thepriniplesoftransriptionregulationweredesribedinthe60'sbyJaobandMonod[24℄.

Experimenting with Esherihia oli, they showed that the transription rate of a spei

genetisequene depends onatleastthree fators: itspromoter,whihisthe initialbinding

sequene of the RNA polymerase, regulation sites (either ativators or inhibitors) where

somespeiproteinsanbind,thereafterinueningthetransriptionproess,andexternal

fators suh asthe onentration of RNA polymerase inthe ell. Note that these priniples

annot be onsidered universal: in eukaryoti organisms, the regulation of transription

ativity depends onmany dierent mehanisms,inludinghromatindynamis.

Contrary to eukaryotes, in whih promoters are generally inative in the absene of

transriptionfators (initiationomplexes are neessary forthe transription tostart and a

naked promoter willbe essentially inative), prokaryoti promoters and RNA polymerase

an diretly interat with one another. In the absene of regulatory elements, a promoter

will have an inherent ativity that mainly depends on its quality. When a promoter has a

primary sequene very similar tothe onsensus sequene, RNA-polymerasean easily bind

toit. The initiationoftransriptionwillthenregularly ourand the intrinsitransription

levelwillbehigh (possibly atamaximum levelif thepromoter has avery goodanity with

thepolymerase). Inthisase,thetransriptionratewillonlydependonextrinsifatorssuh

as the RNA polymerase onentrationand quality orthe transription elongation speed).

If the promoter anity to the RNA polymerase is weak, transription will only rarely

be initiated. The quality of the promoter thus determines the transriptional ground tran-

sription level

β

promoterstrength, maximumtransriptionratebeingbounded byglobalfators suhasthe

polymerase properties and onentration.

The transription level an be modiedby the ation of regulatoryproteins. These pro-

teinsmodify thetransriptionlevels, enhaningorinhibitinggenetransription. Inprokary-

otes, this proess ismainlyused toontrolenergy onsumptioninorder tomaintain agood

balanebetween food availabilityand energy, and to adaptto environmentalhanges.

In prokaryotes,inhibitionorrepression oftransriptionours whenaregulatoryprotein

inhibits the initiation of transription or the elongation of the transript (i.e., repressor

proteins). Ativationoftransriptionourswhenaproteinpromotestransriptioninitiation

[48℄. Whenapromoterisativated,itsativity anonlyriseup toamaximumtransription

level (meaning that intrinsially eient promoters an onlybemarginally enhaned).

Transription fators (ativation and repression proteins) at by binding to spei re-

gionsoftheDNAthatarenearthepromoteroftheproteinthey regulate. Repressorproteins

bindtoaregionalledoperator(alsoalledinhibitoryregion)generallysituateddownstream

from the promoter region. When bound there, a repressor may prevent RNA polymerase

frombindingorblok itsdisplaementalongthe DNAthusdisturbing RNAelongation (g-

ure 1(b)). Ativator proteins target ativator-binding sites are usually loated upstream

of the promoter region. They promote RNA-polymerase binding, thus enhaning protein

prodution(gure 1()).

enhancer p r o m o t e r o p e r a t o r

DNA START STOP

t e r m i n a t o r

(a) Whenno proteinsbind theregulatory regionsthe RNA tran-

sriptionisdoneatgroundlevel.

RNA Polymerase

enhancer p r o m o t e r o p e r a t o r

DNA START STOP

t e r m i n a t o r transcription

translation

(b) Aregulatory proteinhas targetedthe operator. It bloks the

polymerase displaement along DNA and prevents it from tran-

sribingthegene. Thusthistransriptionfatorrepressesthepro-

dutionof theproteinassoiatedwiththisgene.

RNA Polymerase

enhancer p r o m o t e r o p e r a t o r

DNA START STOP

t e r m i n a t o r transcription

translation

() A protein binds the enhaner region, favoring the RNA-

polymerase(toparrow)bindingandtransriptioninitiation. Sine

no inhibitoryproteinbindtheoperator,theRNA-Polymerasean

transribe the gene more eiently, thus enhaning the protein

produtionlevel.

Figure1: Transriptional states inprokaryotes.

Inprokaryotes,multiplegenesoftenshareasinglepromoter,itsoperatoranditsativator

bindingsites. Thesegenesareo-transribedand thereforeo-regulated. Suhasequene in

whihseveral genesshare their promoter and regulatoryregions isalledan operon beause

allgenes are underthe ontrol of asingle operator (gure2).

enhancer o p e r a t o r

START STOP START STOP

p r o m o t e r t e r m i n a t o r

transcription translation RNA Polymerase

DNA

Figure2: Overview of an operon struture

The best known regulation system is probably the Latose (la) Operon whih ontrols

the latose-gluose metabolism in Esherihia oli. When Monod experimented with the

eets ofombiningsugarsasarbonsouresforE. oli,hefound thatif gluoseandlatose

are provided tothe baterium, itrst metabolizesgluose and the olony grows fast. When

gluose is depleted, the bateria stop growing. After a short period (lag-phase), bateria

start onsuming latose and the olony grows again. Jaob and Monod later showed that

this adaptivebehavior omes froma gene regulation mehanism.

InE.oli,thelatosemetabolismisontrolledbyanenzyme,the

β

protein that transports latose from the environment to the ell. The former protein also

onverts part of the latose intoallolatose.

The

β

inueneofthesamepromoterandthesameoperator. Infatthelaoperonontainsathird

gene, LaA,that enodes for a

β

The repressor proteinisable tobindtothe la operator,preventing the transriptionof the

operon(gure3). However, whenlatoseispresentintheell, itinteratswiththe repressor

protein,and hanges itsonformation, preventing itfrombinding tothe la operon. When,

the operon is nolonger repressed LaYand LaZ an betransribed. Due tothe permease,

latoseonentrationthusinreases,while

β

TheLaIontrolisanexampleofnegativeontrol. However,itisnotsuienttoexplain

the whole behavior of the la operon. In partiular, negative ontrol annot explain why,

in presene of both gluose and latose, the operon is not transribed. Indeed, the operon

is alsoontrolledby a positive loop: the onentration of gluose is sensedby the ellvia a

signalingmoleule, AMP;themoregluoseintheenvironment,the lowertheonentration

of AMP. AMP binds to an induer of the operon, the CAP protein, that itself binds on

the DNA upstream from the la promoter. Then, the la operon is transribed if and only

if latose is present in the environment and gluose is not (or no longer) present in the

environment 2

.

2

A lots of seondary mehanisms have been disovered. They slightly modify the behavior of the la

operon but thetwomain regulationloops are thenegativeloop due to LaI and the positive loop due to

AMPbindingonCAP (gure 3).