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Abstract

1.Malaria, dengue, Zika, and other mosquito‐borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide‐based tools and antimalarial drugs. The advent of CRISPR/Cas9‐based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has enabled a wide range of gene drive architectures to be realized, creating a need for their population‐level and spatial dynamics to be explored. 2.We present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially‐explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying userdefined inheritance patterns, b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pests, and c) a landscape module generates the metapopulation model by which insect populations are connected via migration over space. 3.Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9‐based homing gene drive for: a) driving a disease‐refractory gene into a population (i.e. population replacement), and b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing‐resistant alleles in both cases. Further documentation and use examples are provided at the project's Github repository. 4.MGDrivE is an open‐source R package freely available on CRAN. We intend the package to provide a flexible tool capable of modeling novel inheritance‐modifying constructs as they are proposed and become available. The field of gene drive is moving very quickly, and we welcome suggestions for future development.
Methods Ecol Evol. 2019;00:1–11.    
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 1
wileyonlinelibrary.com/journal/mee3
Received:26June2019 
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  Accepted:30September2019
DOI : 10.1111/20 41-210X .1331 8
APPLICATION
MGDrivE: A modular simulation framework for the spread of
gene drives through spatially explicit mosquito populations
Héctor M. Sánchez C.1| Sean L. Wu1| Jared B. Bennett2| John M. Marshall1,3
Thisisanop enaccessarti cleundertheter msoftheCreativeCommonsAttribution‐NonCommercialLicen se,whichpermi tsuse,distri butionandrepr oduction
inanymedium,providedtheoriginalworkisproperl ycitedan disnotuse dforcomm ercialpurposes.
©2019TheAuth ors.Me thods in Ecology and EvolutionpublishedbyJohnWiley&So nsLtdonbehalfofBritishEcol ogicalSociet y.
SeanL .WuandJare dB.Ben nettco ntrib utedeq uallytot hiswork .
1Divisio nofEpidem iolog yand
Biostatisti cs,SchoolofPublic
Health ,Univer sityofC alifornia,Berkeley,
CA,USA
2BiophysicsGraduateG roup,Divisionof
Biologi calSci ences,CollegeofLettersand
Science ,Univer sityofC alifornia,Berkeley,
CA,USA
3InnovativeGeno micsIn stitute,Berkeley,
CA,USA
Correspondence
HéctorM.SánchezC.
Email:sanchez.hmsc@berkeley.edu
JohnM.Marshall
Email:john.marshall@berkeley.edu
Funding information
InnovativeGeno micsIn stitute;UCIr vine
MalariaInitiat ive;Defe nseAdvanced
ResearchProjec tsA gency,Gr ant/Award
Number :HR0011‐17‐2‐00 47
HandlingEditor:NickGolding
Abstract
1. Malaria, dengue, Zika and other mosquito‐borne diseases continue to pose a
major globalhealth burden through muchofthe world, despitethe widespread
distribution of insecticide‐based tools and antimalarial drugs. The advent of
CRISPR/Cas9‐basedgene editingand itsdemonstrated ability to streamline the
development of ge ne drive systems has re ignited interest in th e application of
thistechnologytothecontrolofmosquitoesandthediseasestheytransmit.The
versatilityofthistechnologyhasenabledawiderangeofgenedrivearchitectures
tobe realized,creating aneed fortheir population‐level and spatialdynamics to
beexplored.
2. WepresentMGDr ivE (Mosquito Gene Drive Explorer): asimulation framework
designedtoinvestigatethepopulationdynamicsofavarietyofgenedrivearchi-
tecturesandtheir spread throughspatiallyexplicit mosquito populations. A key
streng th of the MGDrivE f ramework is its m odularity : (a)a gen etic inherita nce
module accommodates the dynamics of gene drivesystems displaying user‐de-
fined inheritancepatterns, (b) a population dynamic module accommodates the
lifehistoryofavarietyofmosquitodiseasevectorsandinsectagriculturalpests,
and(c)alandscapemodulegeneratesthemetapopulationmodelbywhichinsect
populationsareconnectedviamigrationoverspace.
3. Example MGDrivE simulations arepresented to demonstrate the application of
theframeworktoCRISPR/Cas9‐basedhominggenedrivefor:(a)drivingadisease‐
refractorygeneintoapopulation(i.e.populationreplacement),and(b)disruptinga
generequiredforfemalefertility(i.e.populationsuppression),incorporatinghom-
ing‐resistantalleles in both cases. Furtherdocumentation and useexamples are
providedattheproject'sGithubrepository.
4. MGDrivE is an open‐source r packagefreely available on CRAN. Weintend the
packagetoprovideaflexibletoolcapableofmodellingnovelinheritance‐modify-
ingconstructsastheyareproposedandbecomeavailable.Thefieldofgenedrive
ismovingveryquickly,andwewelcomesuggestionsforfuturedevelopment.
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Methods in Ecology and Evoluon
SÁNCHE Z Et al.
1 | INTRODUCTION
Theadvent ofCRISPR/Cas9‐based gene editingtechnology and its
application to the engineeringof genedrive systemshasled to re-
newedexcitementintheuseofgenetics‐basedstrategiestocontrol
mosquito ve ctors of human d iseases and ins ect agricult ural pests
(Champe r, Buchman , & Akbari, 2016; Es velt, Smidler, Cat teruccia,
& Church, 2 014).A pplicatio ns to control mos quito‐bor ne diseases
have gained the most at tention due to the major global health
burden they pose through much of the world and the difficult y of
controlling them using currently available tools ( Walker, Griffin,
Ferguson,&Ghani,2016).
Th eeas eof gen e edi t ing affo r d edb yCR I SPR has a lso ledt os i gni f-
icantversatilityintermsoft hegenedrivesystemsthatarenowreal-
izab le(C ham peretal ., 20 16;M ar sha ll &A kba ri,2018).Th es eincl ude:
(a)homing‐basedsystemsthatcleaveaspecifictargetsitelackingthe
drivesys temandarethencopie dtothissitebyser vingasatemplate
forhomology‐directedrepair(HDR)(Burt,20 03;Windbichleretal.,
2011),( b)r em ed ia ti onsystemsthatco ul dbeusedtoremoveeffector
genesandpossiblydrivesystemsfromtheenvironmentintheevent
ofunwantedconsequences (Gantz& Bier,2014;Marshall&Akbari,
2018), and (c) thre shold‐depen dent systems th at may permit con-
fineable and reversiblereleases (Akbari etal., 2013; Buchman, Ivy,
Marshall,Akbari,&Hay,2018;Marshall&Hay,2012).
Understandinghowthesesystemsareexpectedtobehaveinreal
ecosystemsrequiresafl exiblemodellingframeworkthatcanaccom -
modatearangeofinheritancepatterns,species‐specificdetails,and
landscape details where a construct may be released. To this end,
wep re sentMG DrivE(MosquitoGeneDriveExplorer):aflexiblesim-
ulationframeworkdesignedtoinvestigatethepopulation dynamics
ofavarietyofgenedrivesystemsandtheirspreadthroughspatially
explicitpopulationsofmosquitoesandotherinsects.
MGDrivEi suniq ueinitsabi li t yt os imu la tediver s e, us er‐s pec if ied
inheritance‐modifyingsystemswithinasingle,computationallyeffi-
cientframeworkincorporatinginsectlifehistoryandlandscapeecol-
ogy.Otherexistingframeworksweredesignedforgeneralpurpose
simulationsandappliedtogene drive studies( Table1).For example,
Eckh off(2011)usedtheEMODmalariamodeltosimul atethespread
ofhoming‐basedgenedrivesystemsthrough spatialpopulations of
Anopheles gambiae.EMODisopensourceand apowerfulmodelling
framework;butsignificanteffortisrequiredtoredefinegeneticcon-
trolstrategies,life‐historyparametersandlandscapedetails.Magori
etal.(20 09)createdSkeeterBusterbyextendingtheCIMSiMmodel
(Focks, Da niels, Hail e, & Keesling , 1995)to in corporate ge netic in-
herita nce and spatia l struct ure. Skeeter B uster capt ures most pe r-
tinent mosquitoecology considerations,butisnot opensourceand
can only simulate a handful of geneticcontrolstrategies (Legroset
al.,2012).TheSLiMgeneticsimulationframework(Haller&Messer,
2017)iscapableofmodellingthespreadofuser‐definedgenedrive
systems inametapopulation;however,itdoesnotcurrentlyaccom-
modatelife‐histor yecologyandoverlappinggenerations.
Inthispaper,we describe the keycomponentsoftheMGDrivE
framework – genetic inheritance, mosquito life histor y and land-
scape. We th en demonst rate the appl ication of the f ramework to
CRISPR‐basedhominggenedrivesystemsfor:(a)drivingadisease‐
refractory gene intoapopulation (i.e. population replacement), and
(b)d isrupti ngagenereq uiredforfe ma lefer tili ty(i.e.po pulat io nsu p-
pression),incorporatinghoming‐resistantalleles.Weconcludewith
adiscussionoffutureapplicationsofgeneticsimulationpackagesin
thefieldofgenedrivemodelling.
KEY WORDS
Aedesaeg ypti,Anophelesgambiae,inheritancepattern,landscape,lifehistory,mathematical
model,populationdynamics,rpackage
TABLE 1 Comparisonofspatiallyexplicitgenedrivemodels
Inheritance patterns Life‐history ecology Spatial and landscape details Software
MGDrivE Veryflexible,canbe
user‐specified
Egg‐larva‐pupa‐adult,density‐
dependenceatlarvalstage,not
responsivetoenvironment al
variablesatpresent
Populationsdistributed
inspace,connectedby
migration
rpackage,opensource
EMOD
(Eck hof f,
2011)
Homing‐basedgenedrive,
couldbeex tendedwith
effort
Egg‐larva‐pupa‐adult,density‐
dependenceatlarvalstage,
responsivetoenvironment al
variables
Populationsarrangedon
agrid,eachrepresent-
ing1km2,connectedby
migration
JavaScriptOpen
Notation(JSON)feeds
intoexecutablefile,
opensource
SkeeterBuster
(Legrosetal.,
2012)
Homing‐basedgenedrive,
releaseofinsect scarrying
aconditionallethal,etc.,
cannotbeuser‐specified
Egg‐larva‐pupa‐adult,density‐
dependenceatlarvalstage,
responsivetoenvironment al
variables
Householdsandcontainers
modeledexplicitly,con-
nectedbymigration
Execut ablefile,notopen
source
SLiM(Haller&
Messer,2017)
Veryflexible,canbe
user‐specified
Discretegenerations,nolife
histor yatpresent
Canmodeleitherconnected
populationsorcellsonagrid
Scriptingenvironment
withgraphicaluser
interf ace,opensource
    
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 3
Methods in Ecology and Evoluon
SÁNCHE Z Et al.
2 | DESIGN AND IMPLEMENTATION
The MGD rivE frame work is a geneti c and spatial e xtension of t he
lumped age‐class model of mosquitoecolog y(Hancock&Godfray,
2007)modifiedandappliedbyDeredec,Godfray,andBurt(2011)to
thespreadofhominggenedrivesystems,andbyMarshall,Buchman,
Sánchez C., and A kbari (2017) to population‐suppressing homing
systems in the presence of resistant alleles. The framework incor-
porates the egg,larval, pupaland adultlife stages, withegg geno-
typesdeterminedbymaternalandpaternalgenot ypesandtheallelic
inheritance pattern.In MGDrivE, bytreating the lumped age‐class
model equationsinavariable‐dimensiontensoralgebraicform, the
populationdynamicequationscanbeleftunchangedwhilemodify-
ingthedimensionalityofthetensordescribinginheritancepatterns,
asrequired by the number of genotypes associated with the drive
system.Spatialdynamics aresimulatedbyametapopulationstruc-
ture in which migrants are exchanged between populations with
definedprobabilities. Full detailsofthisframeworkareprovided in
DataS1.
The core framework is developed in r (https://www.r‐proje
ct.org/)withcer tainroutinesinRcppforcomputationalspeed.By
combiningthetensormodelling framework with object‐oriented
programming, the genetic, life history and spatial components
ofthemodel are ableto be separated into‘modules’ to facilitate
easeofmodification.Wenowdescribethethreemodulesinmore
detail.
2.1 | Modules
2.1.1 | Genetic inheritance
Thefundament almoduleforgenedrivedynamicsisthatdescribing
geneticinheritance.InMGDrivE,thisisembodiedbyathree‐dimen-
sional tensor contained in a drive‐specific R fileand referred to as
an‘inheritancecube’(Figure 1).Thefirst andseconddimensions of
theinheritancecube refer tothematernalandpaternal genotypes,
respec tively, and the t hird refers to t he offspr ing genotyp e. Cube
entries f or each combin ation of parent al genotyp es represen t the
propor tion of off spring that a re expecte d to have each genot ype,
and should sum to one, as fitness and viability are accommodated
separately.
The R func tion that build s the inherita nce cube may receive a
numberofuser‐definedinputparameters.Forexample,forahoming‐
based drive system, the list of input parameters should include the
homi ngeff i ci enc y,t her at eo fi n‐fr a me re sis tan ta lle le ge ner ation ,an d
therateofout‐of‐frameorotherwisecostly resistantallelegenera-
tion(Marshalletal.,2017;Unckless,Clark,&Messer,2017).In‐frame
resistant alleles are those forwhich the codingframe ofthe target
site is not alte red, leading t o minimal fitne ss effect s, while out‐of‐
frameresistantallelesdisruptthecodingframe,leadingtosignificant
fitnesseffects. Inputparameters also include those associated with
organis ms having each ge notype – fo r example, ge notype‐ specific :
(a)fertilityrates,(b)malematingfitness,(c)sexbiasatemergence,(d)
FIGURE 1 Inheritancemodule.Geneticinheritanceisembodiedbyathree‐dimensionaltensorreferredtoasaninheritancecube(left),
heredepictedforaCRISPR‐basedhomingconstruc t.Maternalandpaternalgenotypesaredepictedonthex and y‐axesandoffspring
genotypesonthez‐axis,withslicesofthecubeper tainingtoeachoffspringgenotypeshowntotheright.Theinheritancepatternshown
deviatesfromstandardMendelianinheritancesuchthat,inthegermlineofHhparent s,themajorityofwild‐type(h)allelesareconverted
intohoming(H)alleles,whileasmallproportionareconvertedintoin‐frameresistant(R)andout‐of‐frameresistantalleles(B).Forthe
examplepictured,thefrequenc yofaccuratehominggivencleavageinHhheterozygotesis98%,withtheremaining2%ofwild‐typealleles
beingconver tedtoeitherin‐frame(1%),orout‐of‐frame(1%)resistantalleles
4 
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SÁNCHE Z Et al.
adultsur vivalrates,and(e)femaleandmalepupatorysuccess.These
parametersfeedintothemosquitolife‐historymodule,whichwillbe
describ ed next . Finally, a ‘viab ility mas k’ is applie d to the offs pring
genotypestoremoveunviablegenotypesfromthepopulation.
At the time of p ublication, t he MGDrivE pac kage includes in-
herita nce cubes for: (a) st andard Mendel ian inheritanc e, (b) hom-
ing‐baseddriveintendedforpopulationreplacementorsuppression
(Gantzetal.,2015;Hammond etal.,2016;Marshalletal.,2017),(c)
Medea (a maternal tox in linked to a zygoti c antidote) (Chen et al .,
2007),(d)othertoxin‐antidote‐basedunderdominantsystemssuch
asUDMEL (Akbari et al.,2013;Marshall, Pittman, Buchman, & Hay,
2011), (e) reciproca l chromosomal tr anslocations (B uchman et al.,
2018), (f) Wolbachia (Hancock, Sinkins, & Godfray, 2011), and (g)
the RIDL a nd pgSIT sys tems (Kandul et a l., 2019; Wise de Valdez
etal.,2011)(releaseofinsectscarryingadominantlethalgene,and
precision‐guidedsterileinsecttechnique).Detailsof each of these
systems are providedintheonlinedocumentation athttps://marsh
alllab.github.io/MGDrivE/docs/reference/.
2.1.2| Mosquito life history
Th em os quitoli fe ‐h istor ymo dulefol lo wsfro mt he lumpe da ge‐cl as s
model of Ha ncock and Go dfray (2007 ) adapted by De redec et al.
(2011).Inthismodel(depictedinFigure2),theinsec tlifecycleisdi-
vid edintofours tages–egg(E),la rva(L),pupa(P)anda dult(Fforfe -
maleandMformale).InMGDrivE,eachlifes tageisassociatedwit h
agenot ype. Adultfemalesmateonceandproducebatchesofeggs
fromthespermofthesamemale,sotheyobt ainacompositegeno-
type uponmating(theirownandthatof the maletheymatewith).
Egggenotypesarethendeterminedbytheparentalgenotypesand
inheritance pattern as provided in theinheritance cube.The adult
equilibrium population size, N, in a given h abitat patch is u sed to
determine the carrying capacity ofthat patchfor lar vae, K, which
determinesthedegreeofadditionaldensity‐dependentmortalityat
thelarvalstage in thatpatch. FollowingDeredecetal.(2011),this
isdescribed byan equationofthe form:
f(L)=
𝛼
(
𝛼
+L)1TL
,whereL
isthe numberoflarvaeinthepatch,TListhedurationofthelarval
stage, and αisa parameterdescribingthe strength ofdensityde-
pendence.Further detailsonthe mathematical formulationof the
lumped‐ageclass modeland its generalizationtoanarbitrar ynum-
berofgenotypesusingtensoralgebraareprovidedinDataS1.
TheMGDrivEframeworkcurrentlyappliestoanyspecieshav-
inganegg‐larva‐pupa‐adultlifehistoryandforwhichdensity‐de-
pendentregulationoccursatthelarvalstage.Switchingbetween
species canbe achievedbyalteringparametervalues within this
mod ule:(a)th enu mb erofeg gsprodu cedpe radultfe malep erday,
(b)thedurationsofthe egg,larvalandpupal juvenile life stages,
(c)thedailymortalit yriskfortheadultlifestage,and(d)thedaily
population growth rate (in the absence of density‐dependent
mortality).Thedailydensity‐independentmortalityrisksforthe
juvenile s tages are assume d to be identical an d are chosen for
consisten cy with the dai ly population gr owth rate. Def ault life‐
historyparametervaluesareshowninTable2forthreespeciesof
interest:(a)A. gambiae,themainAfricanmalariavector,(b)Aedes
aegypti,themainvectorofdengueandZikavirus,and(c)Ceratitis
capitata, a wo rldwide ag ricultura l crop pest . In some cas es, life‐
historyparametersaremodifiedingenotype‐specificwaysbythe
genedriveconstruc t.Acurrentlimitationoftheframeworkisthat
equilibriumpopulationsize remainsconstantovertime. This will
beaddressedinthenextreleasedversionofMGDriv E.
FIGURE 2 Mosquitolifehistor ymodule.Lifehistoryismodelledaccordingtoanegg(E)‐larva(L)‐pupa(P)‐adult(Fforfemale,Mformale)
lifecycleinwhichdensitydependenceoccursatthelarvalstageandautonomousmobilityoccursattheadultstage.Genotypesaretracked
acrossalllifestages,representedbythesubscript
i∈{1, ..., g}
.Forexample,Mirepresentsthenumberofadultmaleshavingtheithgenot ype.
Femalesaremodelledasmatingonceuponemergenceandobtainacompositegenotype–theirownandthatofthemaletheymatewith.
Egggenot ypesaredeterminedbytheadultfemale'scompositegenotypeandtheinheritancepattern,whichisspecifictothegenedrive
systemunderconsideration
    
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 5
Methods in Ecology and Evoluon
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2.1.3 | Landscape
Thelan ds cap em od uledesc ri besth ed is tribu ti onofmo sq uitopop-
ulations inspace, with movementthroughthe resulting network
determinedby adispersalkernel. Discretepopulationsin there-
sultingmetapopulationarerandomlymixingpopulationsforwhich
the equatio nsoft helum pe dage‐cl assmo de lapply.T heresolut io n
ofthe individualpopulations (in terms of size)should be chosen
according tothe dispersal properties of the insect species of in-
terest and the research question being investigated. A. aegypti
mosquito es, for insta nce, are thought to b e relatively loc al dis-
persers,often remainingin thesamehousehold forthe duration
oftheirlifespan(Schmidt,Filipović,Hoffmann,&Rašić,2018).For
modellingthefine‐scalespreadofgenedrivesystemsinthisspe-
cies, populations onthescale ofhouseholds maybeappropriate.
A. gambiaemosquitoes,ontheotherhand,arethoughttodisplay
moderatedispersalonthevillagescaleandinfrequentinter‐village
movement (Taylor etal., 2001). Thiswouldsuggest villagesasan
appropriatepopulationunit; however otherlevelsofaggregation
are also possible, inboth cases, dependingon the resolutionre-
quiredandcomputationalpoweravailable.
Daily per‐capita movement probabilities between populations
(nodes in thenetwork)for theseexampleswere calculated froma
zero‐inflatedexponentialkernel,accountingforpairwisedistances
betweennodes.Thiskernelmodelsmovementasatwo‐stagepro-
cess,wherebya mosquitofirstdecideswhethertoleavethecur-
rentpopulation(governedbyaparameter,p0,representingthedaily
probabilitythatitremainsinthesamepopulation),andintheevent
of movement , select s the destin ation node fro m the full set wi th
probabilitiesbasedondistanceaccordingtoanexponentialdistribu-
tion(governedbyaparameter,λ,where1/λisapproximatelyequal
tothe mean dispersal distancein a largelandscape). As the simu-
lation only requiresamatrix ofinter‐nodemovement probabilities,
arbitrarily complexkernels that accountforbarriers,suchasroads
(Sc hmidtetal.,2018),mayb eusedwithoutal teringt hemodelar chi-
tec ture.Thematri xofmoveme ntp robab ilitiesi sin corpo ratedint he
tensoralgebraicmodelformulationdescribedinDataS1.
Finally, any t ype of releas e can be simul ated by increas ing the
number of in sects having a gi ven sex and genoty pe at a specific
populat ion and time. A s demons trated in the f ollowing use ex am-
ples,releasesareparameterizedaccordingto:(a)numberofreleased
individuals, (b) numberofreleases,(c)timeoffirst release, (d) time
betweenreleases,(e)populationofrelease,and(f)sexandgenotype
ofreleasedinsects.
2.2 | Deterministic versus stochastic simulations
Simulations can be run either in deterministic or stochastic form.
Deterministicsimulationsarefasterandlesscomputationallyinten-
sive;however,stochasticsimulationscapturetheprobabilisticnature
ofchance events that occurat low population sizes and genot ype
frequencies. In the stochastic implementation of MGDrivE, daily
egg prod uction foll ows a Poisson dist ribution, of fspring gen otype
follows a multinomial distribution informed by parental genotypes
andthe inheritance cube,mate choice followsamultinomial distri-
butiondeterminedbyadultgenot ypefrequencies,and survivaland
death event s follow bino mial distri butions at th e populatio n level.
When interpreting stochastic models, many simulations should be
runtounderstandtherange of outputs possible for a given model
realization.
3 | RESULTS
To demonstrate how the MGDriv E framework can be used to
initialize a nd run a simulat ion of a gene drive sy stem through a
metapo pulation, we have provided vignettes with the package,
available via instillation from CRAN at https://CRAN.R‐proje
ct.org/package=MGDrivE, and additional examples andinforma-
tion on Github atht tps://github.com/MarshallLab/MGDrivE and
thepackageweb site,ht tp s://marshallla b.git hu b.io/M GDrivE/.The
vignettesprovideexamplesofsimple simulations and landscape
setup.TheybeginwithadeterministicexampleofMendelianin-
heritance,andexploreexpectedgenotypefrequenciesaccording
TABLE 2 Life‐historymoduleparametervaluesforthreespeciesofinterest(atatemperatureof25°)
Parameter Symbol Aedes aegypti Anopheles gambiae Ceratitis capitata
Eggproductionper
female(day−1 )
β20(Otero,Solari,&Schweigmann,
2006)
32(Depinayetal.,
2004)
20(Diamantidis,C arey,Nakas ,&
Papadopoulos,2011)
Durationofeggstage
(days)
TE5(Christophers,1960) 1(Depinayetal.,
2004)
2(Diamantidisetal.,2011)
Durationoflarvalstage
(days)
TL6(Christophers,1960) 13(Depinayetal.,
2004)
6(Diamantidisetal.,2011)
Durationofpupastage
(days)
TP4(Christophers,1960) 1(Depinayetal.,
2004)
10(Diamantidisetal.,2011)
Dailypopulationgrowth
rate(day−1)
rM1.175(Simoy,Simoy,&Canziani,
2015)
1.096(Molineaux&
Gramiccia,1980)
1.031(Carey,Liedo,&Vaupel,1995)
Dailymor talityriskof
adultst age(day−1 )
μF,μM0.090(Fay,1964;Focks,Haile,
Daniels,&Mount,1993;
Horsfall,1955)
0.123(Molineaux&
Gramiccia,1980)
0.100(Nyamukondiwa,Weldon,Chown,le
Roux,&Terblanche,2013)
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to Hardy‐Weinber g equilibrium, before violating some of these
assumptions. Next, they explore the effects of genotype‐spe-
cific fi tness costs o n genotype tr ajectories a nd population s ize.
Theimpactofstochasticityonmodelpredictionsisthenexplored
throughstochasticsimulations,withdynamicsbeingcomparedto
thoseexpectedfromequivalentdeterministicsimulations.
He re ,wed e sc rib et h ea ppl ic ati onofthe pa cka getot wo homin g
gene drive s trategies : (a)d riving a dise ase‐refra ctory ge ne into a
population(Gantz et al.,2015),and(b)disruptinga generequired
forfemalefertilityandhencesuppressingapopulation(Hammond
etal.,2016). Inbothcases,we considerapopulation of A. aegypti
mosquitoeshavingthebionomicparametersprovidedinTable2and
dis tr ibute dthroug hthen et workl an dscaped ep ic tedinFigur e3.To
demonstratethefunctionalityoftheMGDr ivEpackage,wemodel
bothstrategies using the deterministic andstochastic implemen-
tations.In both cases, we include the generation of in‐frame and
out‐of‐frameorother wise costly resistant alleles (Champer et al.,
2017)and parameterize the genedrive model based on recently
engineeredconstruct s(Gantzetal.,2015;Hammondetal.,2016).
3.1 | Population replacement
WebeginbymodellingaCRISPR‐basedhomingconstructsimilarto
that engineeredby Gant z et al. (2015). This was the first CRISPR‐
based hom ing constru ct demons trated in a mos quito diseas e vec-
tor – namely, Anopheles stephensi, the main urban malaria vector
in India. Fo r this constr uct, ho ming and resi stant all ele generat ion
were shown to o ccur at diffe rent rates in mal es and female s, and
therewerelargefitnessreductionsassociatedwithhavingthehom-
ingconstruct.Weconsiderahomingefficiencyof90%inmalesand
50% in females – i.e. 90% of wild‐type (h) alleles areconvertedto
homing(H)allelesinthegermlineofHhmales,and 50%ofhalleles
areconverted toHallelesinthegermline ofHhfemales. Athirdof
the remaining h alleles in Hh individualsareconverted to in‐frame
resistantalleles(R),andtheremainderareconvertedtoout‐of‐frame
orotherwise costlyresistant alleles(B)due to error‐pronecopying
duringthehomingprocess(Champeretal.,2017).Femalefecundity
andmalematingfitnessarereducedby25%perHorRalleleandby
50%perBallele.
The general workflow for the simulation is shown in
Figure 4, with thefullcodeavailableat https://github.com/Marsh
allLab/MGDrivE/tree/master/Examples/.Webeginbyloadingthe
MGDrivE package in randsettingtheworkingandoutputdirec-
tories.Wethenchoose between the deterministicandstochastic
implementationofthemodel–inthiscase,thedeterministicver-
sion.Next,wespecifythebionomicparametersofthe specieswe
aremodelling –in this case, A. aegypti, whosedefaultlife‐history
parameters are provided in Table2.Followingthis, we definethe
landsc ape through whic h we will model the sp read of the drive
system.WebeginbyloadingaCSVfilecontainingthecoordinates
(longitudeandlatitude)of thepopulationsinFigure 3. A function
isthenappliedthatcomputesdailymovementratesbetweeneach
ofthe populationsbased onazero‐inflated exponential dispersal
kernel, the parameters for which we provide. Equilibrium adult
FIGURE 3 Landscapemodule.
Insect saredistributedaspopulations,
heredepictedbynodes,eachhaving
theirowncoordinatesandpopulation
size.Movementbetweenpopulationsis
derivedfromadefineddispersalkernel,
depictedherebyedgesbetweennodes.
Theexamplescenarioallowsbothspread
withinandbetweencommunitiesto
beexplored.Here,nodesarecoloured
accordingtotheircommunity(detected
bytheDBSCANclusteringalgorithm,
Daszykowski&Walczak,2010),with
sizesproportionaltotheir‘betweenness
centrality’–ameasureoftheir
connectednesstoothernodesinthe
metapopulation(Freeman,1978)
    
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populationsizescanbeprovidedforeachofthepopulations;how-
eve rinthiscase,wea ss um et he se ar eidentic al an dp rovideasi ng le
populationsize(Codesample1).
Wi tho ur lif eh ist oryand lan dsc ape mod ule sd efin ed,we no w
specifythegenedrivesystemandreleasestrategyweintendto
mo del(C ode sam ple 2) .Weu se ap r e‐ spe cif ied inh eri tan cecub e ,
‘Cube_HomingDrive()’,thatmodelstheinheritancepatternofa
homing‐basedgenedrive system.Wespecifysex‐specifichom-
ingrates,resistanta llelegenerationr atesa ndgenot ype‐sp ec if ic
fi t nes sef f ect s bas e do nth eco nstr u cte ngi n eer edb yG a ntze ta l .
(2015).Wethenspecifythereleaseschemebygeneratingalist
conta ining:(a)t hereleasesize,(b)nu mb erofr el eases,(c)t im eof
firs trelease,and(d)timebet weenreleases .Thisisincorporated
into a vec tor also specifying the inheritance cube and the sex
andgenot ypeofthereleasedinsects.Finally,thepopulationsin
whichthereleasetakesplacearespecified.Withthesimulation
frameworknowfullyspecified,themodelisreadytorun(Code
sample3).
FIGURE 4 WorkflowofanMGDrivE
simulation
Code sam ple 1:Loadingthep ackagea ndsett ingup th elifehistor yand
landscapemodules.
Code sample 2:Settinguptheinheritance/genedrivemoduleandde-
fining thereleasescheme.Here, codeis shown forboth: A) homing‐
basedreplacementdrive,andB)suppressiondrive.Onlyoneofthese
shouldbeselectedwhenrunningthesimulation.
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3.2 | Population suppression
As a second example, we demonstrate the application of the
MGDrivE package to a population suppression homing construct
similar tothat engineered by(Hammondetal.,2016).For this con-
struct,thehomingsystemtarget sagenerequiredforfemalefertil-
ity, causin g females lac king the gene (th ose having the g enotype s
HH,HBandBB)tobeinfert ile,andindu cin galargefe cundityreduc-
tionof90%infemalesonlyhavingonefunctioningcopyofthegene
(those having thegenotypesHh,HR, hB and RB). The homingeffi-
ciencyisveryhigh–99.9%inbothmalesandfemales–withathird
oftheremaininghallelesinHhindividualsbeingconvertedRalleles
andtheremainderbeingconvertedtoBalleles.Thisissimilartothe
first C RISPR‐based ho ming constr uct demo nstrated in A . gambiae,
althoug h with a higher homin g efficiency t hat could be achieved
throughguideRNAmultiplexing(Marshalletal.,2017).Linesofcode
that differ for this systemare shownin Code sample 2.While the
sameinheritancecubeapplies,specificparametersdiffer– namely,
homingandresistantallelegenerationrates, andgenotype‐specific
fitnesseffects.
4 | OUTPUT ANALYSIS
Inth ec ur ren tv er sio nofM GDr ivE,completesimulationresultsare
out putasC SVfiles,twobasi cplottingfunctionsa reprovi de dinR ,
FIGURE 5 ExampleMGDrivEsimulationsforCRISPR‐basedhomingconstructs.Inbothcases,anAedes aegyptipopulationissimulated
havingthebionomicparametersinTable2anddistributedthroughthelandscapedepictedinFigure3.Deterministicsimulationsare
denotedbysolidlinesinpanels(a)and(b),whilestochasticsimulationsaredenotedbythinlines,eachcorrespondingtotheoutputofa
singlesimulation,anddottedlines,correspondingtothemeanof100stochasticsimulations.(a)Apopulationreplacementhomingconstruct
thatdrivesadisease‐refrac torygeneintothepopulationissimulatedhavingahomingefficiencyof90%inmalesand50%infemales.Wild‐
type(h)allelesthatarenotconvertedtohoming(H)allelesinthegermlineofHhheteroz ygotesarecleavedandconvertedtoeitherin‐frame
(R)orout‐of‐frame(B)resistantalleles.Femalefecundityandmalematingfitnessarereducedby25%perHorRalleleandby50%perB
allele.Asinglereleaseof100HHfemalesatnode6ismodeled.Asthehomingallele(blue)isdrivenintothepopulation,thewild‐typeallele
(red)iseliminated,andthein‐frameresistantallele(purple)accumulatestoapopulationfrequencyof17%.(b)Apopulationsuppression
homingconstruc tthatinterfereswithagenerequiredforfemalefertilityissimulatedhavingahomingefficiencyof99.9%inbothfemales
andmales.Wild‐typeallelesthatarenotconvertedtohomingallelesinthegermlineofHhheterozygotesarecleavedandconver tedto
eitherin‐frameorout‐of‐frameresistantalleles.FemaleswithoutacopyofthehorRalleleareinfertile,whilefemaleshavingonlyonecopy
ofthehorRallelehavea90%fecundityreduction.Fivereleasesof100HHfemalesatnode6aremodeled.Asthehomingallele(blue)is
drivenintothepopulation,itsuppressesthepopulationduetoitsimpactonfemalefertility.Eventually,anin‐frameresistantallele(purple)
emergesandleadsthepopulationtoreboundduetoitsselectiveadvantageoverbothwild‐typeandhomingalleles.(c,d)Population
frequenciesofthewild‐type,homingandin‐frameresistantallelesareshownineachpopulationovertimeforadeterministicmodelof
thepopulationreplacementconstruct(panelc)andastochasticsimulationofthepopulationsuppressionconstruct(paneld).Out‐of‐frame
resistantallelesareomit tedduetotheirlowfrequenciesinbothsimulations.Dashedverticallinesrepresentthebeginningandendofthe
releases
Code sample 3:Preparingoutputfoldersandrunningthemodel.Itisrec-
ommendedtostor esimul ationfil esfore achru ninitsownsepar atefolde r.
    
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andseveral functionsareprovided for aggregatingthedata – by
population,genotype,orsomecombinationthereof–asrequired
bythequestionofinterest.
In F igur e 5,w e dis p l aya p oten t i alv i s ual i z atio n s chem e p rodu c e d
in Mathematica for the simulations described above (additional
videos are providedin the Supplementary Information: S1Video
andS2Video).Wedepictallelecountonthey‐axisintheFigure5a
andbandallelefrequency(depictedascolourdensity)inFigure5c
andd,withtimeonthehorizontalaxis.Forpopulationreplacement
(Figure 5a andc),wesee the gene drive allele (H) spread through
thepopulation,andthein‐frameresistantallele(R)accumulatetoa
smallextent.Thisoccursbecausethe Rallele hasneither afitness
cost norbenefit relative to the H allele once it has saturated the
population,whiletheBalleleisselectedagainstduetoitsinherent
selectivedisadvantage.Stochasticity slowsthese dynamics, onav-
erage,andintroducesvariabilityaroundthemean(Figure5a).
Forpopulation suppression(Figure 5band d), we see the gene
drivesystem(H)spreadthroughthepopulationatthesametimeasit
inducessuppressionduetoitsimpactonfemalefertility.Eventually,
wesee an in‐frame resistant allele(R) emerge and spread into the
populat ion due to its s electi ve advantage ove r both the wil d‐type
andhomingalleles.Inthedeterministicmodeloutput,thein‐frame
resistantallelespreadstofixation;howeverinthestochasticmodel
output,thehomingalleleisoftenlostfromthepopulationand,asa
result,theselectiveadvantageofthein‐frameresistantalleleislost,
causingittoequilibrateatalowerpopulationfrequencythaninthe
deterministicsimulation(inwhichitisneverlost).Stochasticityalso
significantlyslowsthemeanallelefrequencytrajectories,aswellas
introducing variabilit y around the mean. Mathematica and Python
filestogenerateFigure5areprovidedathttps://github.com/Marsh
allLab/MGDrivE/tree/master/Examples.
5 | FUTURE DIRECTIONS
WearecontinuingdevelopmentoftheMGDrivEsoftwarepackage,
andwelcomesuggestionsandrequestsfromtheresearchcommu-
nity reg arding futur e directions . The field of gene d rive has been
movingveryquickly,especiallysincethediscoveryofCRISPR‐based
geneediting,andweintendt heM GD rivEp ackagetop rov ideafl exi-
bletooltomodelnovelinheritance‐modifyingconstructsastheyare
proposedandbecomeavailable.Futurefunctionalitywillinclude:(a)
‘shadow drive’,inwhich the Cas9 enzyme is passed on tothe off-
spring evenif the gene expressing it isnot (Champer et al., 2017),
(b)life‐historymodelsincorporatingarangeofdensity‐dependence
relationships,andencompassingamorediverserangeofinsectdis-
ease vec tors and agricult ural pests, a nd (c) populations tha t vary
in size seaso nally or in resp onse to environ mental dri vers such as
temperatureandrainfall.Incorporationofenvironment aldriverswill
allowbothsea sonaltre ndsandshor t‐ter mfluc tuati onstob eac com -
modatedwithinthesameframework.
Additionally, we are developing a corresponding individual‐
based model that iscapable of modelling multi‐locussystems for
which the number of possible genotypes exceeds the number
of individ uals in the pop ulation. T his will enabl e us to efficie ntly
model confineable systems suchas daisy‐drive involvingseveral
loci(Noble,Olejarz,Esvelt,Church,&Nowak,2017),andmultiplex-
ingschemesinwhichasinglegeneistargetedatmultiplelocations
tore du cethe rateofres ist antal lel ef or mat ion(P ro wseetal .,2017 ).
ACKNOWLEDGEMENTS
TheauthorsthankDr.OmarAkbari,Dr.EthanBierandDr.Anthony
Jamesfordiscussionsongenedrivearchitecturesandmolecularbio-
logical c onsideratio ns, and Dr. Gregor y Lanzaro, D r.Yoosook L ee,
Dr. Gordana Ra šić and Partow I mani for discussio ns on mosquito
ecolog y, life histor y and dispersa l behaviour. This work was sup-
portedbyaDARPASafeGenesProgramGrant(HR0 011‐17‐2‐0 047)
awarded to J.M.M. andfunds fromtheUC Irvine MalariaInitiative
andInnovativeGenomicsInstituteawardedtoJ.M.M.
AUTHORS’ CONTRIBUTIONS
H.M.S.C. and J.M.M. conceivedtheproject.H.M.S.C. led MGDrivE
developmentandS.L.W.andJ.B.B.contributedsubstantiallytocore
de ve lop m ent .H . M .S.C .an dJ . M.M .wr oteth efi r std raf tof thema nu-
script.J.B.B.andS.L.W.wrotethevignettes.Allauthorsrevisedthe
manuscriptandapprovedforpublication.
ORCID
Héctor M. Sánchez C. https://orcid.org/0000‐0001‐7378‐8853
John M. Marshall https://orcid.org/0000‐0003‐0603‐7341
DATA AVA ILAB ILITY STATE MEN T
MGDrivE version 1.1.0 is available on CRAN at https://CRAN.R‐
project.org/package=MGDrivE. Additional examples and plotting
scripts are available on Github at https://github.com/MarshallLab/
MGDrivE, and the package website, https://marshalllab.github.io/
MGDrivE/.The sourcecode is available underthe GPL3 License and
is free for other groups to modif y and extend as needed (https://
doi.org/10.5281/zenodo.3479781).Mathematicaldetailsofthemodel
formulationareavailableinDataS1,anddocumentationofallMGDrivE
functions,includingvignettes,areavailableattheproject'sGithubre-
pository at https://marshalllab.github.io/MGDrivE/docs/reference/.
Torunthesoftware,werecommendusingrversion3.4.4orhigher.
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
SupportingInformationsection.
How to cite this article:SánchezC.HM,WuSL,BennettJB,
MarshallJM.MGDrivE:Amodularsimulationframeworkfor
thespreadofgenedrivesthroughspatiallyexplicitmosquito
populations.Methods Ecol Evol. 2019;00:1–11. h t t p s : / / d o i .
org /10.1111/2 041‐210X.13318
... Larvae The mosquito GD explorer (MGDrivE v 1.6.0) was used to model the inheritance of the 443 AeaNosC109 GD and AeaZpgC109 GD lines (Sánchez et al. 2020). A stable population was set to 444 10000 mosquitoes at a 1:1 sex ratio with no migration out of, or into, the main patch. ...
... Based on the experimentally derived data regarding GD inheritance including frequency of GDBI 715 accrual, testing for maternal effect of the CRISPR/Cas9 RNP, and analysis of fitness parameters 716 of the GD harboring mosquito lines (Table S3), we modeled the performance of AaeNosC109 GD 717 or AeaZpgC109 GD in a hypothetical field release scenario for the purpose of population 718 replacement (Figure 7; Table S4, Table S5). To model the lines, we used the MGDrivE mosquito 719 population modeling package in R (Sánchez et al. 2020) and modeled for a static population of 720 ...
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... To determine whether iterative releases of fertile Ifegenia males could facilitate population suppression and elimination, we incorporated the above data on Ifegenia performance into a mathematical model (26) and simulated releases into a population of 10,000 A. gambiae adults (Figure 2). Weekly releases of up to 500 Ifegenia eggs (female and male) per wild-type adult (female and male) were simulated over 1-48 weeks. ...
... To model the expected performance of Ifegenia and pgSIT at suppressing and eliminating local A. gambiae populations, we used the MGDrivE simulation framework (26). This framework models the egg, larval, pupal, and adult mosquito life stages with overlapping generations, larval mortality increasing with larval density, and a mating structure in which females retain the genetic material of the adult male with whom they mate (sperm) for the duration of their adult lifespan. ...
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... 或者选 择能加入特定物种的生活史参数的软件, 以进行更有 针对性的驱动模拟. 如Marshall团队 [8,11,21] 利用融入了 蚊虫生活史生态学的R语言程序包MGDrivE(Mosquito Gene Drive Explorer) [64] , 预测了分离驱动、携带重编 Figure 3 The simulation study of gene drive, take the split drive system as an example. A: Using the experimental data of the few generations in the cage as parameters to simulate the driving effect of the split drive system in multi-generation and large populations. ...
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Gene drive refers to the phenomenon that specific genes or genetic elements are passed from parent to offspring in the form of super-Mendelian inheritance. In recent years, based on the genetic characteristics of gene drive and the theoretical basis of their molecular mechanisms, and supported by CRISPR/Cas9 gene-editing system, gene-driven genetic control technology has become an advanced research hotspot in the field of basic mosquito biology and genetic control. And there have emerged some practical and effective achievements that take into account ecological stability. This paper reviews the basic principles of gene drive, CRISPR/Cas9-mediated and HDR-type drive technology strategies, improved strategies to reduce gene-driven resistance and potential risks, and simulation analysis of gene drive. It is hoped to provide a reference for the development of a gene-driven mosquito genetic control technology system that takes both high efficiency and safety into consideration.
... Malaria Journal (2022) 21:226 replacement gene drive mosquitoes (classic and integral), as well as with and without other forms of vector control are quantified. Previous modelling work has focused on understanding changes in vector populations with release of GM mosquitoes without considering other types of vector control and without also examining the downstream effects on malaria transmission within corresponding human populations [31,[33][34][35][36][37]. An advantage of this model [32,38] is that it can simulate the effects of gene drive-induced vector population changes on malaria transmission within a realistic human population directly making it possible to quantify the gene drive system and other logistical release parameters needed to reach full malaria elimination. ...
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... Characterizing mosquito movement is important to understanding the 23 spatial transmission of vector-borne diseases [10], and to designing optimal biocontrol 24 strategies, such as those involving Wolbachia, for vector-borne disease control. By 25 analyzing close-kin pairs, these two studies estimated mean dispersal distances in 26 agreement with previous mark-recapture studies [7,11], and isolated a radius of 27 dispersal specific to Ae. aegypti oviposition behavior [6]. 28 In this paper, we extend the CKMR formalism described by Bravington et al. [1] to 29 mosquitoes, using Ae. ...
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... Modeling analysis. Model fitting was carried out using a discrete-generation adaptation of the Mosquito Gene Drive Explorer (MGDrivE) 62 . A likelihood-based Markov chain Monte Carlo (MCMC) procedure was used to estimate gene drive efficacy and genotype-specific fitness costs, employing initial parameter projections from the single-pair test crosses providing Maximum a Posteriori estimates and 95% credible intervals for each parameter 63 . ...
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The alteration of wild populations has been discussed as a solution to a number of humanity’s most pressing ecological and public health concerns. Enabled by the recent revolution in genome editing, clustered regularly interspaced short palindromic repeats (CRISPR) gene drives—selfish genetic elements that can spread through populations even if they confer no advantage to their host organism—are rapidly emerging as the most promising approach. However, before real-world applications are considered, it is imperative to develop a clear understanding of the outcomes of drive release in nature. Toward this aim, we mathematically study the evolutionary dynamics of CRISPR gene drives. We demonstrate that the emergence of drive-resistant alleles presents a major challenge to previously reported constructs, and we show that an alternative design that selects against resistant alleles could greatly improve evolutionary stability. We discuss all results in the context of CRISPR technology and provide insights that inform the engineering of practical gene drive systems.
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Replacement of wild insect populations with transgene-bearing individuals unable to transmit disease or survive under specific environmental conditions using gene drive provides a self-perpetuating method of disease prevention. Mechanisms that require the gene drive element and linked cargo to exceed a high threshold frequency in order for spread to occur are attractive because they offer several points of control: they bring about local, but not global population replacement; and transgenes can be eliminated by reintroducing wildtypes into the population so as to drive the frequency of transgenes below the threshold frequency required for drive. Reciprocal chromosome translocations were proposed as a tool for bringing about high threshold population replacement in 1940 and 1968. However, translocations able to achieve this goal have only been reported once, in the spider mite Tetranychus urticae, a haplo-diploid species in which there is strong selection in haploid males for fit homozygotes. We report the creation of engineered translocation-bearing strains of Drosophila melanogaster, generated through targeted chromosomal breakage and homologous recombination. These strains drive high threshold population replacement in laboratory populations. While it remains to be shown that engineered translocations can bring about population replacement in wild populations, these observations suggest that further exploration of engineered translocations as a tool for controlled population replacement is warranted.
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The recent discovery of CRISPR and its application as a gene editing tool has enabled a range of gene drive systems to be engineered with greater ease. In order for the benefits of this technology to be realized, in some circumstances drive systems should be developed that are capable of both spreading into populations to achieve their desired impact, and being recalled in the event of unwanted consequences or public disfavor. We review the performance of three broad categories of drive systems at achieving these goals - threshold-dependent drives, homing-based drive and remediation systems, and temporally self-limiting systems such as daisy-chain drives.
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Self-replicating gene drives that can spread deleterious alleles through animal populations have been promoted as a much needed but controversial ‘silver bullet’ for controlling invasive alien species. Homing-based drives comprise an endonuclease and a guide RNA (gRNA) that are replicated during meiosis via homologous recombination. However, their efficacy for controlling wild populations is threatened by inherent polymorphic resistance and the creation of resistance alleles via non-homologous end-joining (NHEJ)-mediated DNA repair. We used stochastic individual-based models to identify realistic gene-drive strategies capable of eradicating vertebrate pest populations (mice, rats and rabbits) on islands. One popular strategy, a sex-reversing drive that converts heterozygous females into sterile males, failed to spread and required the ongoing deployment of gene-drive carriers to achieve eradication. Under alternative strategies, multiplexed gRNAs could overcome inherent polymorphic resistance and were required for eradication success even when the probability of NHEJ was low. Strategies causing homozygotic embryonic non-viability or homozygotic female sterility produced high probabilities of eradication and were robust to NHEJ-mediated deletion of the DNA sequence between multiplexed endonuclease recognition sites. The latter two strategies also purged the gene drive when eradication failed, therefore posing lower long-term risk should animals escape beyond target islands. Multiplexing gRNAs will be necessary if this technology is to be useful for insular extirpation attempts; however, precise knowledge of homing rates will be required to design low-risk gene drives with high probabilities of eradication success. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
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Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.
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CRISPR/Cas9 gene drive (CGD) promises a highly adaptable approach for spreading genetically engineered alleles throughout a species, even if those alleles impair reproductive success. CGD has been shown to be effective in laboratory crosses of insects, yet it remains unclear to what extent potential resistance mechanisms will affect the dynamics of this process in large natural populations. Here we develop a comprehensive population genetic framework for modeling CGD dynamics, which incorporates potential resistance mechanisms as well as random genetic drift. Using this framework, we calculate the probability that resistance against CGD evolves from standing genetic variation, de novo mutation of wildtype alleles, or cleavage-repair by nonhomologous end joining (NHEJ) -- a likely byproduct of CGD itself. We show that resistance to standard CGD approaches should evolve almost inevitably in most natural populations, unless repair of CGD-induced cleavage via NHEJ can be effectively suppressed, or resistance costs are on par with those of the driver. The key factor determining the probability that resistance evolves is the overall rate at which resistance alleles arise at the population level by mutation or NHEJ. By contrast, the conversion efficiency of the driver, its fitness cost, and its introduction frequency have only minor impact. Our results shed light on strategies that could facilitate the engineering of drivers with lower resistance potential, and motivate the possibility to embrace resistance as a possible mechanism for controlling a CGD approach. This study highlights the need for careful modeling of the population dynamics of CGD prior to the actual release of a driver construct into the wild.