Table Of ContentMicrobiology(2013),159,1236–1253 DOI10.1099/mic.0.067975-0
Tuning the dials of Synthetic Biology
Review
James A. J. Arpino,1,2,3 Edward J. Hancock,4 James Anderson,4,5
Mauricio Barahona,3 Guy-Bart V. Stan,1,6 Antonis Papachristodoulou4
and Karen Polizzi1,2
Correspondence 1CentreforSyntheticBiologyandInnovation,ImperialCollegeLondon,SouthKensingtonCampus,
AntonisPapachristodoulou LondonSW72AZ, UK
[email protected] 2Department of LifeSciences, Imperial College London,South Kensington Campus,London
SW72AZ,UK
3Department of Mathematics, Imperial College London, SouthKensington Campus,
LondonSW72AZ, UK
4Department of Engineering Science, University ofOxford, Parks Road, Oxford OX13PJ,UK
5StJohn’s College, StGiles, Oxford OX13JP, UK
6Department of Bioengineering, Imperial College London, SouthKensington Campus,
LondonSW72AZ, UK
Synthetic Biologyis the‘Engineering ofBiology’ –it aimsto usea forward-engineering design
cycle basedon specifications, modelling, analysis, experimental implementation, testing and
validation tomodify natural or design new, synthetic biology systems sothatthey behave ina
predictable fashion. Motivatedby the needfor truly plug-and-play synthetic biological
components, wepresent a comprehensive reviewofwaysinwhich the various partsofa
biologicalsystemcanbemodifiedsystematically.Inparticular,wereviewthelistof‘dials’thatare
availabletothe designer anddiscusshowthey can bemodelled, tunedandimplemented. The
dialsarecategorizedaccordingtowhethertheyoperateattheglobal,transcriptional,translational
orpost-translational levelandthe resolution thatthey operate at.Weend this reviewwitha
discussionon therelative advantagesanddisadvantages of somedialsover others.
Introduction Although Synthetic Biology has been described as the
‘Engineering of Biology’, a systematic design cycle is still
The primary goal of Synthetic Biology is to create new or
not realized to its full potential, limiting the advancement
add additional functionality to biological systems by
of the field in terms of functionality, reliability and size of
constructing new parts, or modifying existing biological
the genetic systems (Purnick & Weiss, 2009). A design
systems(Purnick&Weiss,2009).Centraltothisgoalisthe
framework involves design specifications, modelling, con-
idea that the synthetic organism is designed following a
ceptualanddetaileddesign,aswellasimplementationand
systematic design framework with a specific objective in
testing (Fig. 1). In Synthetic Biology, carrying out
mind designed a priori. Ideally such design objectives
conceptual design (e.g. choosing the basic genetic system
can be formulated in a quantitative manner so that the
performanceofthedesigned componentcanbe quantified layout)iscurrentlyrelativelysimpleduetothelimitedsize
and compared to the original design specification. This of present-day synthetic genetic systems, but this will
designframeworkisrequiredbothtoimprovereliabilityof becomemoreinvolvedasmorecomplicatedsystemscanbe
individualbiologicalcomponentsandtobuildfunctioning built (Purnick & Weiss, 2009; Slusarczyk et al., 2012).
genetic systems with a larger number of interconnected Similarly, methods are being developed to design modules
parts (Purnick & Weiss, 2009), both considered to be for spatial organization of the cell (Chau et al., 2012; Lim
current challenges of Synthetic Biology. Currently, one of et al., 2012), metabolic pathways and microbial com-
themaineffortsofSyntheticBiologyisonbuildinggenetic munities(Shongetal.,2012).Atthesametime,thepresent
systems in micro-organisms, not only because of their design framework needs to be improved with respect to
relative simplicity but as it is envisioned that small how specifications, more detailed design and robust
genetic circuits can potentially be used as a foundation implementation are performed. An improved forward-
for building more complex systems (Andrianantoandro engineering framework would consist of a mathematical
et al., 2006). modelofthesystemchosenintheconceptualdesignstage,
1236 067975G2013SGM PrintedinGreatBritain
TuningthedialsofSyntheticBiology
In vivo 1. Design objectives In
In vitro and specifications: silico
A. Inputs and outputs Standardized
B. System performance database of
biological parts
7. Testing and 2. Design according 3. System
characterization to spec: models composed
of the system A. Conceptual design from parts
B. Detailed design
4. In silico verification: LuxR-AHL
A. Analyse models AHL Cell-to-cell
5. Implementation luxl yemGFP coupling
B. Simulate/predict
A. DNA assembly
behaviour
B. DNA synthesis
aiiA
6. Evolution
Fig.1.Aproposedforwardengineeringdesigncycle.Steps1–4takeplaceinsilicoandfollowaclassicalengineeringdesign
approach: specification, design, modelling and analysis. Steps 5, 6 and 7 take place in the laboratory where the system is
assembled,mightbeevolvedfortunedbiologicalfunction,andischaracterized.Thecyclecanbeiteratedifthedesigndoesnot
performtothespecifications.AdaptedfromMacDonaldetal.(2011).
which can provide a basis for the design, construction, modifiedsystematically,focussinginparticularonbuilding
characterization and testing of the developed system. The genetic systems. We first discuss design and modelling of
parameters in this model can then be ‘tuned’ in a genetic systems, before reviewing in detail the typical dials
systematic manner in order to ensure that the resulting that can be modified in a Synthetic Biology project. We
modelmeets thedesign specifications. Themodelwiththe then present various ways to tune these dials in order to
chosenparametersandpredictedperformancecanbebuilt achieve a desirable objective and show how tuning the
and its behaviour can then guide subsequent design, parameters for each of these dials affects the output of a
implementation and testing. simple genetic system.
However,thisiseasiersaidthandone.Indeed,when‘tuning’
the different biological dials it is important to fully
System design and parameter tuning
understand the relationship between specifications, model
Synthetic Biology aims to be the ‘Engineering of Biology’,
parameters,biologicalpartsandimplementationinorderto
where an engineering design cycle is usedto systematically
carry out the design process. The dials used to redesign a
improve existing biological systems and create new ones
biological system can include tuning global parameters or
(Anderson et al., 2012). A traditional engineering example
transcriptional, translational and post-translational para-
isthedesignofachemicalplant.Inthiscasespecifications
metersinthemathematicalmodels.Experimentallythiscan
may include the concentrations of the final products, a
be achieved by using different plasmid replicons for
conceptual design may determine the order of processes
controlling gene copy number, different promoters to
and reactions, while a more detailed design may set
control the rate of transcription initiation, different ribo-
variables such as concentrations and flow rates in these
some-bindingsites(RBSs),ordifferentsynonymouscodons
processes, followed by further component details based on
for controlling translation levels or degradation rates of all
these variables such as sizes of pipes and reaction vessels
the species in the systems. The models used for the basic
(Perry&Green,1999).Similarly,inabiologicalsystem,the
design of genetic systems often contain parameters repre-
specificationsmaybe based onproteinconcentrationsand
senting multiple biological parts and so tuning a parameter
their response characteristics, while a conceptual design
inamathematicalmodelcanbeimplementedexperimentally
determinesthelayoutofageneticsystemneededtoachieve
indifferentways.Forexample,inthesimplestmodelsofgene
the specifications. A more detailed design may tune some
expression, one parameter often represents many different
of the parameters in the mathematical model(s), such as
biologicalcharacteristics,suchasa‘transcription’parameter
biochemical rate constants, followed by the design of
representing promoter strength,transcriptionrate and gene
individual biological parts fulfilling these parameters such
copy number. Each of these has different tuning ranges,
as the design of a RBS to achieve a particular translation
uncertaintiesandeaseofexperimentalmodification.
rate.Inthisframework,relevantmodelsaredevelopedand
In this paper, we present a comprehensive review of ways analysed at the different design stages in order to evaluate
in which the various parts of a biological system can be the candidate designs and predict whether they will meet
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J.A.J.Arpinoandothers
specifications. Once a detailed design is completed and analytical methods (Murray, 2002; Tyson et al., 2003) can
verified,thesystemcanbebuiltandthentestedtovalidate then be used to verify the behaviour of the models. In
thedesign,withthedesigncyclerepeatedifatanystagethe particular, stochastic simulations are very useful in testing
resulting functionality is not acceptable or requires thevariabilityofthesystemduetonoise,andtoensurethat
improvement (RAEng, 2009). stochastic effects do not substantially change the system
behaviour for low biochemical species numbers (Tian &
Thefirststepinthedesignofageneticsystemistospecifyits
Kevin,2006;Wilkinson,2011).Oncedesignparametersare
functionality for defined inputs and outputs. For example,
selected, further models may be required for component
thesystem may berequiredtoact asa memory deviceora
design, such as for designing a RBS to match a tuneable
switch (Gardner et al., 2000) where the input is the
parameter (Na et al., 2010). The design process may need
concentration of an inducer and the output is the
iteration, so that if no feasible choice of parameters for a
concentration of a protein. Performance specifications are
particular system can meet the specifications, then a
required in order to determine whether the functionality is
different topology can be used. Furthermore, once the
met for a particular design (Sen & Murray, 2012). These
systemisimplementedandtested,theprocessmayneedto
specifications can be composed of several metrics (Canton
be iterated. In particular, furtherdetailed design or‘tuning
et al., 2008;DelVecchio et al., 2008; Sen & Murray, 2012).
ofthedials’maybenecessaryforthecircuittofunctionand
Foraswitch,forexample,therecouldbearequirementfor
meetspecifications.
the (time) mean protein concentration to be between set
limits when the switch is ‘on’ or ‘off’. Retroactivity Discussion of the genetic system design leads naturally to
specifications, or insulation, may also be required. This the question of implementation, the main focus of this
ensures that the functionality of the genetic system is review. Which biological components should be modified
not negatively affected when it is connected to another in order to implement different genetic systems?
systemupstreamordownstream(DelVecchioetal.,2008).
Anothermetriccouldbetheresponsetime:thetimeittakes
for the protein concentration to switch from low to high A simple genetic system
withachangeininducerconcentration(Cantonetal.,2008). Before we discuss this question, let us consider a simple,
Also,theremay berequirementsonthelimitsofvariability illustrative genetic system and its associated model as an
or noise around the mean of a protein concentration level example (Fig. 2). This system will be used throughout the
(Lestasetal., 2010). Finally, a geneticsystemdesign should paper to illustrate the engineering design cycle and how
meet allperformancemetrics despitenoiseanduncertainty ‘tuning dials’ can be performed during the design and
associatedwiththecomponentsandchassisofthesystem,as implementationstages.Theexamplegeneticsystemcontains
wellastheuncertaintyincellsizeduetogrowth. aconstitutivepromoterdrivingtheexpressionofarepressor
protein,whichinturnrepressestheexpressionofareporter
Once specifications are set, the design of a genetic system
gene from a regulated promoter. The measured output of
consists of a conceptual phase (e.g. determining genetic
system topology) and then using appropriate models to thesystemistheconcentrationofthereporterproteinwhile
complete a more detailed design. The latter involves theinputistheconcentrationofaninducer,whichbindsto
determining model parameters to meet the design the repressor protein thereby sequestering it away and
specificationsset.Intheconceptualphase,differentsystem allowingtranscriptioninitiation.Thebiochemicalequations
topologies can be used to obtain a desired behaviour, e.g., usedtomodelthissystemareshowninFig.2.
oscillators (Purcell et al., 2010; Slusarczyk et al., 2012;
Thebiochemicalequationsarethemathematicaldescription
Strelkowa & Barahona, 2010), switches (Pfeuty & Kaneko,
oftheunderlyingbiochemicalreactionsofthesystem.From
2009) and adaptive systems (Ma et al., 2009), and more
abiologicalviewpoint,thereactionsthatmustbedescribed
complicated systems can be built to produce more
are: transcription, translation, repressor–promoter and
advanced behaviour (Slusarczyk et al., 2012). In the
repressor–inducer interactions, and degradation of species
detailed design phase, a mathematical model must first
within the system. Equations (1) and (2) describe RNA
bebuiltandanalysed.Thismodelwillguidethedesignbut
polymerasebindingtoapromoterfollowedbytranscription
also be used for predicting whether a proposed design
initiation fortherepressorandreporter genes,respectively.
meetstherequiredspecifications.Thesamemodelwillalso
Initiationoftranscriptionisareversiblereaction(asdenoted
beusedforstepsafterdesignsuchascomparisonwithdata
bythedoublearrowsandforwardandreversereactionrate
fromthetestingphaseoftheengineeringdesigncycle.Such
constantsintheequations),whereasextensionisconsidered
models usually take the form of differential equations
to be irreversible. Equation (3) is included to reflect the
based on the biochemical reactions defining the designed
biologicalrealitythatmostpromotershavesomebasallevel
system (Wilkinson, 2011). These differential equations can
of transcription in the absence of an inducer (also called
be deterministic or stochastic. The design proceeds by
leakiness). Taken together, these equations describe the
using standard optimization and control engineering
generationofmRNAspeciesinthesystem.
approaches on the deterministic models to find the best
parameter choice that achieves a desired objective. A Equations(4)and(5)describethebindingofribosomesto
combination of both simulations (Wilkinson, 2011) and a RBS on mRNA, before translation is initiated for the
1238 Microbiology159
TuningthedialsofSyntheticBiology
P1 Degradation accountedforseparatelyfromthetranslationrate,whichis
RBS1 tag usuallytakenasaconstantnumberofaminoacidsperunit
Repressor Ori time. Equations (4) and (5) together describe the rate of
generation of protein species in the system.
Theinteractionsoftherepressor withthepromoter andthe
Inducer inducer control the number of free promoters available for
Degradation
RBS2 tag RNApolymerasebinding.Theseinteractionsaredescribedin
P2 Reporter Ori equations(6)–(9).Equation(6)describesdimerizationofthe
repressorprotein,basedinthisexampleonTetR,toproduce
Biochemical reactions: itsfunctionalform,whichiscapableofbindingtheoperator
Transcription region of a promoter and repressing transcription. Other
ω ω
g + Pol 1 g Pol 2g + Pol + m (1) repressorsformdifferentfunctionalmultimers(e.g.LacIacts
Con ω Con Con Rep
–1 as a tetramer) and would require additional equations to
ω ω
g + Pol 3 g Pol 4 g + Pol + m (2) reflect the further multimerization steps where necessary.
Rep ω Rep Rep Rpt
–3 Equation(7)describesthebindingofthefunctionalrepressor
ω
gRepPRep2+ Polω 5 gRepPol+ PRep2 (3) proteintotheoperator,whileequation(8)describesinducer
–5 binding to the free repressor, which in turn prevents its
Translation
ω ω bindingtoDNA.Equation(9)describesinducerbindingtoa
mRep+ R ω 6 mRepR 7PRep+mRep+ R (4) repressor that is already bound to an operator, followed by
ω –6 ω dissociation of the inducer–repressor complex from the
mRpt+ Rω 8 mRptR 9 PRpt+mRpt+ R (5) operator,allowingtranscriptiontoproceed.
–8
Repressor dimerization
Finally, equation (10) describes the degradation of the
ω
PRep + PRepω 10 PRep2 (6) mRNA andprotein speciesinthe system. The degradation
–10 contributes tothe steady state concentration of the species
Repressor–operator binding
by ensuring its removal.
ω
gRep + PRep2ω 11 gRepPRep2 (7) From this set of biochemical reactions, mass-action
–11
Inducer–repressor binding kinetics can be used to produce a deterministic model
ω
12 from the biochemical equations (Cornish-Bowden, 2004)
I + PRep2ω IPRep2 (8) while the chemical master equation can be used for a
–12
ω ω
I + gRepPRep2ω –1133IgRepPRep214gRep + IPRep2 (9) smtoocdheal,sttihcemmoadsse-lac(tGioilnlekspiniee,ti1cs99c2an).bFeoursethdetoddeteesrcmribineistthiec
Degradation different reaction rates, while differential equations
β β β γ γ
PRpt Rptφ, PRep Repφ, PRep2Rep2φ, mRpt Rptφ, mRep Repφ (10) describe the rates of change of the concentrations due to
the reactions. For the stochastic model, the equations
describe the probability of a reaction occurring, e.g. an
Fig. 2. A simple genetic network with model equations. A increasedreactionconstantmakesthatreactionmorelikely
schematicrepresentationofasimplegeneticnetworkhighlighting to occur at higher concentrations.
dialsthatcanbetunedexperimentally;promoters(P1andP2:blue
and pale orange arrows), ribosome-binding sites (RBS1 and
Characterizing dials
RBS2:redandpinktriangles),repressorgene(orangerectangle),
reportergene(greenrectangle),proteindegradationtags(yellow) What sets Synthetic Biology apart from traditional
and origins of replication (black and grey). Expression of the molecular biology is that an engineering design approach
repressor results in repression of P2 whilst addition of inducer is taken to systematically design or redesign a biological
activates P2. Biochemical reactions: a set of biological reactions system, for quantifiably improved or different function-
describing the processes that take place in the simple genetic ality. This assumes that a design objective is set, against
network,whereg istheconstitutivepromoterdrivingexpression
Con whichtheperformance ofthesynthetic component willbe
of the repressor gene, g is the regulated promoter driving
Rep quantified (Anderson et al., 2012). To achieve the design
expressionofthereportergene,PolistheRNApolymerase,m ,
Rpt objective,asetofbiological‘dials’needtobetuned.Inthis
m are the reporter and repressor mRNA, P , P are the
Rep Rpt Rep section,wedescribethesetofpossibledialsavailabletothe
reporterandrepressorproteins,Iistheinducer,Ristheribosome,
designer, and how they can be described mathematically.
and all other species are complexes resulting from biochemical
interactions between the above listed elements. The symbol w
denotesdegradationofaspecies. Global parameters
Chassis.When designing, constructing and characterizing
repressor and reporter, respectively. These steps have been simple single gene expression constructs through to
describedastworeactionssothattheRBSstrength(which complex genetic networks, it is important to consider the
is easy to modify experimentally – see below) can be impact of strain-to-strain variation of the behaviour of
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J.A.J.Arpinoandothers
these networks. It has been shown that the abundance of multi-copyplasmidsaremaintainedatameanvalueineach
RNA polymerases and ribosomes is dependent on cellular cell, leading topopulation variations (Ebersbach & Gerdes,
growth rates, affecting downstream processes such as 2005)thatwillneedtobeaccountedforinmodellingefforts.
transcription rate, translation rate, gene copy number, Althoughplasmidcopynumbershavebeen describedhere,
mRNA degradation, protein dilution, protein degradation they are dependent on the context within which they are
and cell mass (Klumpp et al., 2009). Regulated processes used.Thecopynumberofplasmidshasbeenshowntohave
suchaspromoter repression oractivationhavealsoshown an inverse relationship to the plasmid size (Ebersbach &
growth rate dependence, affecting genetic networks as a Gerdes, 2005; Zhong et al., 2011) and cellular growth rate
result (Klumpp et al., 2009; Scott et al., 2010; Tan et al., (Klumpp, 2011), and their maintenance within a cell is
2009).Thereareawiderangeofstrainsandorganismsthat dependent on the toxicity of the genes encoded on the
can be used to harbour synthetic genetic networks, and in plasmid(Mileykoetal.,2008).
some cases, the networks work predictably across different
strains (Prindle et al., 2012) whilst in other cases the
behaviour of the network can be drastically altered by Transcription level design
changing the host cell (Egbert & Klavins, 2012). In this
Promoter type. Promoters are regions of DNA contain-
latter reference, two strains of E. coli were used that both
ing consensus sequences for the recruitment of the tran-
contained a LacID mutation but with slightly different
scriptional machinery (sigma factors; RNA polymerase,
genotypes, exemplifying that even similar strains can have
RNAP) to initiate transcription (Gruber & Gross, 2003).
profound effects on network function. Some tuneable
Corepromotersequencesarerecognizedbydifferentsigma
elements may also be strain specific and whilst they
factors which are active under different environmental
functioninonestraintheymaybeinactiveinanother,e.g. conditions,forexample,Escherichiacolis70,sS,s32ands54
the DIAL strains used to modulate plasmid copy number
(Table2)(Gruber&Gross,2003).Thenumberofsfactors
(Kittleson et al., 2011).
used in bacteria can vary from 1 (in Mycoplasma
genitalium) to 63 (in Streptomyces coelicolor) and provides
Gene copy number. The number of gene copies can be
an intricate way for organisms to regulate gene expression
used to increase or decrease the amount of available
in response to specific environmental conditions.
protein over a partially linear range. Gene copy number
Promoters can also be constitutively active or regulated
can be controlled either by changing the origin of
(Fig.3).Constitutivepromotersareactivewithouttheneed
replication of a plasmid-bound gene of interest or by
for any transcription factors, whilst regulated promoters
increasingthenumberofchromosomallyintegratedcopies.
haveoperator-bindingsitesforrepressorsoractivatorsthat
Plasmidcopynumberhasbeenshowntoplayakeyrolein
block or assist RNAP binding respectively in the presence
the behaviour of genetic networks (Atkinson et al., 2003;
of a small molecule (inducer) or under certain
Mileyko et al., 2008). The range of accessible values is
environmental conditions (Lloyd et al., 2001) (Table 2).
limited to a maximum value that does not saturate the
Combinatorial promoter design has been implemented to
cellular transcription and translation machinery and does
generate hybrid promoters (also sometimes called logic
not cause undue metabolic burden on cells (‘run-away’
gates) that are conditionally activated in the presence of
replication) (Nordstro¨m & Uhlin, 1992).
multiple inducer signals (Fig. 3) (Cox et al., 2007).
Gene copy number is not a continuous number.
Chromosomal integration has been shown to tolerate up
Inducer concentration. Each transcriptional repressor/
tofivecopiesofthesamegene(Choietal.,2006),although
activator will respond to one or more small molecules
this could potentially be increased further, and plasmids
known as inducers. There are natural inducers (e.g. allo-
have a few discrete values that can be accessed (Table 1).
lactose for the Lac repressor (Lewis et al., 1996) or
Engineering endeavours have produced plasmids with
tetracyclinefortheTetrepressor(Orthetal.,2000)),andin
inducible copy number that can be controlled either by
some instances non-metabolizable chemical analogues
the binding of a ligand (Panayotatos, 1984) or through
that cause gratuitous induction (e.g. isopropyl-b-
changes in temperature (Sternberg, 1990), allowing
thiogalactoside, IPTG, for the Lac repressor (Lewis et al.,
dynamic shifts in copy number to be used as a design
1996) or anhydrotetracycline, aTc, for the Tet repressor
variable. Alternatively, multiple bacterial strains have been
(Lederer et al., 1996)). The advantage of the chemical
developed (DIAL strains) that maintain the same plasmid
analoguesisthattheir concentrationlevelremains roughly
at different steady state copy numbers (Kittleson et al.,
constant. The level of transcription follows a sigmoidal
2011). These techniques give another level of control and
response to the inducer concentration, which, over a
tuneability of plasmid copy number in genetic systems.
certain range, can be approximated as linear (Table 3).
The potential to maintain multiple plasmids, encoding Often the slope of this linear approximation is very large,
different components from genetic networks, at different which may make tuning difficult. Mutations in the small
copy numbers within a cell is also possible. This is, moleculebindingsiteoftherepressorcouldshifttherange
however, dependent on the incompatibility group of the over which the response is linear (Satya Lakshmi & Rao,
plasmid(Table1)(Tolia&Joshua-Tor,2006).Inaddition, 2008), adding further control.
1240 Microbiology159
TuningthedialsofSyntheticBiology
Table1.Plasmid copynumber and plasmidincompatibility groups
Plasmidincompatibilitygroupsarehighlighted.
Plasmid Replicon Copynumber* Reference
ColE1 ColE1 15–20 Schmidt&Inselburg(1982);Shizuyaetal.(1992)
pBR322 pMB1 15–20 Balba´setal.(2013);Lin-Chaoetal.(1992);Sternberg(1990)
pBR3722 pMB1 30–40D Hakkaartetal.(1985);Kool&Nijkamp(1974);Lin-Chaoetal.(1992)
pUC pMB1 .200D Figurskietal.(1979);Lin-Chaoetal.(1992)
pACYC p15A 18–22 Chang&Cohen(1978);Hasunuma&Sekiguchi(1979)
F1 F1 1–2 Peterson&Phillips(2008);Shizuyaetal.(1992)
pNS358 P1lyticori 1–.25 Peterson&Phillips(2008);Sternberg(1990)
pCDF CloDF13 10–90 Hakkaartetal.(1985);Kool&Nijkamp(1974);Ku¨es&Stahl(1989)
RK2 RK2 4–7 Figurskietal.(1979);Kittlesonetal.(2011)
pSC101 pSC101 ~5 Hasunuma&Sekiguchi(1979);Kittlesonetal.(2011)
pJPA12 pSC101 27 Deuschleetal.(1986);Gruber&Gross(2003);Peterson&Phillips(2008)
pJPA13 pSC101 ~240 Becker&Hengge-Aronis(2001);Peterson&Phillips(2008)
pRSF RSF1030 10–60 Grossmanetal.(1987);Ku¨es&Stahl(1989);Wang&deHaseth(2003)
pBjk2992-jtk2541 ColE2 1–60 Bernardoetal.(2009);Bucketal.(2000);Kittlesonetal.(2011)
pBjk2993-jtk2541 R6K 5–250 Kittlesonetal.(2011);Schleif(2000)
*Plasmidcopynumberisdependentonplasmidsize,genetoxicityand,insomeinstances(indicatedbyD),bytemperature.
Promoter leakiness and basal expression. The function in the repressed state spanning more than two orders of
of promoters is often thought of encompassing two states, magnitude(Murphyetal.,2007).Thestrengthofrepressor
either on or off. However, this is very rarely the case, with binding to its operator sites can affect promoter leakiness
transcriptioninitiationstilloccurringevenintherepressed and can be tuned through mutation of the operator DNA
state (Lanzer & Bujard, 1988). Often this behaviour is not sequence or the DNA-binding domain of the repressor
desirable for inducible promoters. However, it can be protein (Nichols & Matthews, 1997; Penumetcha et al.,
beneficialifaparticularlevelofbasalexpressionisrequired 2010). Another way with which basal transcription can be
intherepressedstatebutcanbefurtherincreasedwiththe tunedisthroughtheco-expressionofthetargetgenefrom
additionofaninducer.Theleakinessofapromotercanbe a constitutive promoter as well as a regulated promoter,
tuned through the location (Lanzer & Bujard, 1988; although this will also result in an increase in gene
Murphy et al., 2007), number (Murphy et al., 2007) and expression level in the induced state.
sequence(Alperetal.,2005;Lanzer&Bujard,1988)ofthe
operatorbindingsites(Fig.4)andcorepromoterelements. Promoter strength. Promoter strength can be tuned by
A combinatorial TetR repressedpromoter library has been creating promoter libraries randomized in the RNAP-
described previously with varying operator multiplicity binding regions affecting RNAP-binding affinity (Alper
and location resulting in promoters with leaky expression etal.,2005;Brewsteretal.,2012;Jensen&Hammer,1998),
Table2.Constitutive, negative, positiveand hybridregulated promoter types
Promoter s Regulatory Regulator Inducer Reference
factor type*
Pbla s70 Con. NA NA Deuschleetal.(1986);Gruber&Gross(2003)
csiD sS Con. NA Starvation/hyperosmolarity Becker&Hengge-Aronis(2001)
PGroE s32 Con. NA Heatshock/nutrientstarvation Grossmanetal.(1987);Wang&DeHaseth(2003)
Po s54 Con. NA Nitrogen/nutrientstarvation Bernardoetal.(2009);Bucketal.(2000)
P s70 Pos. AraC Arabinose Schleif(2000)
Bad
P s70 Neg. LacR IPTG Lutz&Bujard(1997)
LlacO-1
P s70 Neg. TetR aTc Lutz&Bujard(1997)
LtetO-1
P s70 Neg./Pos. LacR/AraC IPTG/arabinose Lutz&Bujard(1997)
lac/ara-1
*Con.,constitutive;Pos.,positiveregulation;Neg.,negativeregulation.
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J.A.J.Arpinoandothers
RNAP + Repressor/
Promoter Sigma Strength activator Effects Reference
Becker & Hengge-Aronis (2001);
–35 –10 s70 – – Bernardo et al. (2009);
Promoter –35 –10 sS – – Binding of BCuocxk eett aal.l . ((22000070));;
different RNAP Deuschle et al. (1986);
type –35 –10 s32 – – sigma factors Grossman et al. (1987);
Gruber & Gross (2003);
–24 –12 s54 – – Lloyd et al. (2001);
Wang & DeHaseth (2003)
Mutation/ –
spacing of RNAP –
recognition – RNAP: promoter Alper et al. (2005);
elements – affinity Brewster et al. (2012);
– Promoter strength Ellis et al. (2009);
Jensen & Hammer (1998)
Mutation of –
intervening –
sequences –
Type of repressor/
Operator
activator
binding Alper et al. (2005);
Promoter strength Lanzer & Bujard (1988);
site Promoter leakiness Lutz & Bujard (1997);
Murphy et al. (2007);
Hybrid + Multiple inducers NPicehnoulsm &et Mchaat tehte awl.s ( (21091907));;
repression + Promoter strength Schleif (2000)
/activation + Promoter leakiness
Promoter Context independent
insulation Promoter strength Davis et al. (2011)
Fig.3. Theeffect ofpromoter architecture on promoter strength, regulation and basal transcription (leakiness). Aschematic
representationofdifferentpromoterarchitecturesthatcanmodulatethestrength,regulationtypeandleakinessofapromoterby
changingthecoresigmafactor(orangeandblueovals),bindingsequences(bluerectangles with”35and”10or”24and
”12recognitionsequencesinredandyellow),interveningsequences(pink),operatorrecognitionsequences,multiplicityand
location(greenrectangles),repressor/activatortypes(greencircles,trianglesorhexagons)orflankingsequences(shadesof
turquoise).
in the operator region, which changes the strength of the will be obtained through the creation of a randomized
interaction of therepressor/activator with theDNA (Alper promoter library, the promoter strength can typically be
et al., 2005), or in the DNA sequences flanking the treated as a continuous variable for modelling purposes.
promoter, reducing any upstream/downstream context Randomized promoter libraries have elicited numerous
dependent effects on promoter strength (Davis et al., constitutive and regulated promoters with activities
2011) (Fig. 4). Mutation of the DNA sequences between spanning over two orders of magnitude (Alper et al.,
important binding motifs has also shown that a linear 2005; Davis et al., 2011; Ellis et al., 2009; Jensen &
variationinpromoterstrengthcanbeachieved(Ellisetal., Hammer,1998).Alimitationintheuseofthesepromoters
2009; Jensen & Hammer, 1998). Although discrete values as predictable parts is the lack of a standardized promoter
Table3.Inducers andtheir workingconditions
Promotertype Repressor Inducer Workingcondition Reference
Lac LacI IPTG 1024–1023M Lutz&Bujard(1997)
Lac LacI* IPTG 1025–1024M SatyaLakshmi&Rao(2008)
Lac LacI* Temperature 30–40uC Yabutaetal.(1995)
Tet TetR aTc 5–50ngml21 Lutz&Bujard(1997)
P AraC Arabinose 1024–1021M Mirasolietal.(2002)
BAD
P /P cI857 Temperature 30–42uC Menartetal.(2003);Villaverdeetal.(1993)
L R
P LuxR AHL 1029–1026M Koch(2005);Urbanowskietal.(2004)
Lux
*LacImutantsdevelopedtorespondtodecreasedIPTGconcentrationoralternativestimulus.
1242 Microbiology159
TuningthedialsofSyntheticBiology
(a) Termination
loop
Inducer
RNAP
Transcription
Transcription
termination
5′ uuuuu 3′ 5′ uuuuu 3′
RBS Gene
(b) RBS
sequesteredRibosome
Inducer
Translation
No translation
5′ 3′ 5′ 3′
Gene
(c)
Ribosome
taRNA
3′ 5′ Translation
crRNA No translation
5′ 3′ 3′ 5′ 3′
RBS
Gene
sequestered
Fig. 4. Transcriptional and translational control by riboregulators. A schematic representation of transcriptional control by a
riboswitch(a),andtranslationalcontrolbyariboswitch(b)oratrans-activatingRNA(taRNA)(c).
strength metric. Promoters can often perform differently 1987). In principle, this transcription delay increases
from how their original characterization would suggest, linearly with the length of the superfluous genes added in
due to differences in experimental conditions and front of the gene of interest and can be approximated as a
measurement equipment. Therefore predicting the continuous variable – although, strictly speaking, this is a
behaviour of a gene regulatory network component such discrete variable whose values are multiples of the time it
as a promoter across different laboratories can be difficult. takes to transcribe a single base (though very long mRNA
The need for a promoter strength metric for the accurate constructswilltendtohavelargertranslationaleffects).An
comparison of promoters produced from different increase in the length of a transcript also has a positive
libraries, experimental conditions and laboratories has influenceontheamountoftranslationfromthefirstgenein
resultedinthedevelopmentofatechniquetostandardizea an operon (Lim et al., 2011). This is due to the fact that
promoter strength with respect to a reference promoter, transcription and translation take place simultaneously in
and quantifying this relative strength in terms of relative prokaryotes. Therefore, thefirst genes inan operon have a
promoter units (Kelly et al., 2009). longer period for translation during transcription before
RNAP dissociation and mRNA degradation (Lim et al.,
Placement of genes in a multi-gene construct or 2011).
operon. The length of time it takes to transcribe a gene
depends upon the gene length (RNAP transcribes at a rate
of ~50 bp s21 (Gotta et al., 1991; Vogel & Jensen, 1994)) Translation level design
and its distance from the transcription initiation site. Ribosome-binding site (RBS) strength. The efficiency of
Therefore,byplacingoneormoregenesinfrontofthegene translation initiation depends on the recruitment of
of interest it is possible to introduce a delay in the ribosomes to the Shine–Dalgarno sequence, which can be
transcriptionprocess.Itisimportanttonote,however,that stronger or weaker depending on variations in this
theremaybeunintendedeffectsonthetranslationprocessif sequence and the distance from the translation initiation
the secondary structure of the mRNA molecule produced codon (Chen et al., 1994; Osterman et al., 2013). The RBS
causesribosomestodisengagebeforetranslationofthegene strength is also dependent on the upstream (Komarova
of interest, or if secondary structure elements are etal.,2005)anddownstreammRNA sequence(Salisetal.,
introduced which alter the stability of the mRNA 2009) due to the formation of local secondary structures
molecule (Carrier & Keasling, 1997a; Newbury et al., that can influence or inhibit ribosome binding. Prediction
http://mic.sgmjournals.org 1243
J.A.J.Arpinoandothers
of the strength of prokaryotic Shine–Dalgarno sequences sizes to mRNAs has been shown to increase the mRNA
can therefore be facilitated by the use of Chris Voigt’s half-lifebetween5-and10-folduptoahalflifeof~24min
simulationpredictionprogram(RBScalculator)(Salisetal., (Arnold et al., 1998; Hansen et al., 1994). Appending 39
2009)orGyooYeolJung’sUTRDesigner(Seoetal.,2013). REP sequences or insertion of REP sequences into
Over 100 predicted RBSs have been experimentally tested intercistronic regions of polycistronic operons can also
showing that the translation initiation rate can be stabilize upstream mRNA transcripts by ~3-fold (Newbury
controlled over a 100000-fold range (Salis et al., 2009). etal.,1987).
The Ouyang lab used the RBS calculator to design RBSs
withpredictedstrengthsforuseinapredeterminedbistable Riboregulators. Riboswitches are RNA genetic control
toggleswitchexemplifyingtheusefulnessofthistool(Chen elements that modulate gene expression in response to an
etal.,2012).Fine-tuningofagenetictoggleswitchhasalso inducer molecule (Vitreschak, 2004) or transacting RNA
been demonstrated by altering the length of the spacer (taRNA) (Isaacs et al., 2004) without the requirement of
between the Shine–Dalgarno sequence and the start codon any RNA–protein interactions. Since their discovery, a
(Egbert & Klavins, 2012). Comparisons of experimental numberofsyntheticriboswitcheshavebeendevelopedthat
data with RBS calculator predictions were in fairly good controlgeneexpressionbyeitherprematuretranscriptional
agreement dependent on the spacer sequence makeup termination (Wachsmuth et al., 2013) or by translational
(Egbert & Klavins, 2012). inhibition by sequestering RBSs (Dixon et al., 2010; Lynch
etal.,2007;Toppetal.,2010)inadose-responsivemanner
Codon optimization. Because of the degeneracy of the to specific inducers (Fig. 4). Riboswitches that control
geneticcode,itispossibletocreatemRNAtranscriptswith premature transcription termination have been shown to
differingsequencethatencodethesameprotein,eliminating elicit up to a 3-fold change in transcription in response to
rare codons and increasing translational efficiency. An an inducer (Wachsmuth et al., 2013), whilst riboswitches
altered coding sequence can also contribute to different that modulate translation initiation have been developed
mRNA secondary structures and, therefore, translational thatspana2-to150-foldrangeinresponsetoaninducer.
efficiency. Whilst standard codon optimization techniques A model-directed redesign of a translational riboswitch
aimtomaximizeproteinproductionthroughusingthemost has also been used to predictively adjust its efficiency
abundantcodonsobservedforhighlyexpressingnativehost (Beisel & Smolke, 2009). The taRNA riboregulators work
proteins(codonadaptationindex,CAI)(Angov,2011),this by the binding of the taRNA to a cis-repressed mRNA
technique does not take into account several factors that (crRNA) resulting in the release of the RBS, allowing
influence translational efficiency: translational pausing translation initiation (Isaacs et al., 2004) (Fig. 4). taRNA
(Angov, 2011), local mRNA secondary structure (Kudla riboregulatorshavebeenutilizedincontrollingametabolic
et al., 2009) and tRNA abundances (Welch et al., 2009). pathway and showed a ~1- to 200-fold increase in
Kudla et al. have shown a correlation between codon translation initiation in the presence of the trRNAs
optimization and the secondary structure of the mRNA at (Callura et al., 2012; Isaacs et al., 2004). Whilst the
the beginning of a gene (regions 24 to +37) with the riboregulators described here do not require RNA–protein
translationalefficiencyinE.coli,witha250-foldvariationin interactions for their function, the CRISPRi platform for
GFP expression across the constructs they tested (Kudla transcriptional repression utilizes ribonucleoproteins (Qi
et al., 2009). Progress has been made in predictive et al., 2013). Briefly, a small guide RNA (sgRNA) is
algorithms that take into account codon usage and tRNA expressed with complementary base pairing to a target
abundance to optimize a gene’s coding sequence to give a DNA sequence and a secondary structural stem–loop that
desired translation efficiency (Welch et al., 2009). This is recognized by a catalytically inactive RNA-binding
codon optimization algorithm could potentially be protein, Cas9. Together the sgRNA-Cas9 ribonucleo-
combined with RNA secondary structure prediction protein binds the target DNA sequence and inhibits
programs in order to facilitate a more accurate prediction initiation of transcription, elongation or transcription
intheresultingefficiencyoftranslation. factor binding depending on where the sgRNA is targeted
(Qi et al., 2013).
mRNAdecayrate.The longevity of the mRNA transcript
is controlled by its secondary structure in the un-
Transcriptional, translational and post-translational
translated regions, which protect it (Bouvet & Belasco,
design
1992;Carrier&Keasling,1997b;Mackie,2012)ormakeit
more vulnerable (Bouvet & Belasco, 1992) to degradation Inteins. Inteins are the protein-splicing equivalents of
byRNases,andthroughefficientbindingandtranslationby introns found in eukaryotic pre-mRNAs. An intein is a
ribosomes blocking RNase action (Carrier & Keasling, genetically encoded element within a target gene and is
1997b; Komarova et al., 2005; Osterman et al., 2013). The transcribed and translated together with the target protein
half-lifeformostmRNAsinE.coliisrelativelyshortat~1– before it undergoes autocatalytic self-excision and splicing
2 min (Mackie, 2012). The longer-lived an mRNA ofthetargetproteinexteins(Gogartenetal.,2002)(Fig.5).
molecule is, the more translation will occur from each Inteins, therefore, work at both a transcriptional and
transcript. Appending 59 stem–loop structures of varying translational level by increasing the time it takes to
1244 Microbiology159
TuningthedialsofSyntheticBiology
transcribe and translate a target gene. Bacterial inteins complexes (Andersen et al., 1998; Dougan et al., 2002;
range in size from 36 to 1986 amino acids (Perler, 2002), Flynn et al., 2001; Hoskins et al., 2000; McGinness et al.,
theoretically increasing transcription by ~2–120 s and 2006; Purcell et al., 2012; Wang et al., 2007). However,
translation by ~2–100 s. A comprehensive list of inteins Cookson et al. have shown that an abundance of protein
andtheirsizescanbefoundathttp://tools.neb.com/inbase/ targeted to the ClpXP machinery can lead to a queuing
list_prop.php. Inteins would be beneficial for engineering effect, which, in turn, leads to a slower rate of protein
delayintogeneticnetworks,inparticulartuningoscillators degradationthatisdependentontheoverallconcentration
that depend on transcriptional and translational delay for oftaggedspeciesinthesystem(Cooksonetal.,2011).This
their function (Mather et al., 2009; Purcell et al., 2010; can be detrimental to genetic network behaviour when a
Stricker etal.,2008).Split inteinshavealsobeen described fast turnaround of network elements is required but can
where the intein domain is transcribed and translated by also be beneficial in coupling separate networks through
two separate genes and the resulting proteins can undergo the queuing effect (Cookson et al., 2011). By utilizing
trans-splicing to produce a single functional protein multiple degradation pathways, the queuing effect could
(Elleuche & Po¨ggeler, 2010; Lockless & Muir, 2009) potentially be suppressed.
(Fig. 5). Post-translational control of inteins has been
engineered to modulate intein splicing (Skretas, 2005), Protein activity. Manipulation of protein activity through
trans-splicing (Mootz & Muir, 2002) or a combination of point mutations can be used as a means of control with a
both(Shi&Muir,2005)inthepresenceofsmallmolecule fewdiscretevalues.Ifthesystemconsistsentirelyofgenetic
inducers (Fig. 5). elements (repressors or activators), then modification of
theDNA-bindingaffinity isbestachieved bymanipulating
Protein level design the DNA sequence, rather than attempting to mutate the
Protein degradation. The longevity of proteins within a protein. However, for enzymic activities, mutants with
cell can be tuned by addition of degradation tags at the N alteredsubstratespecificity(Brannigan&Wilkinson,2002;
(Gur & Sauer, 2008) or the C terminus (Andersen et al., Wilson & Agard, 1991), kinetics (Brannigan & Wilkinson,
1998; Flynn et al., 2001; Gur & Sauer, 2008; McGinness 2002) or thermostability (Lehmann & Wyss, 2001) may
et al., 2006) as well as internally (Gur & Sauer, 2008). already be available or can sometimes be created via
Different degradation tags can be used to target proteins protein engineering (Brannigan & Wilkinson, 2002;
for degradation to one of several cellular degradation Lehmann & Wyss, 2001; Wilson & Agard, 1991).
complexes (Kirstein et al., 2009) (Table 4). Modifying the
Extension to eukaryotic dials
N-terminal residues of a protein can also target it for
proteolytic degradation by the N-End rule mechanism The majority of the dials described above can also be used
(Erbse et al., 2006; Kirstein et al., 2009). Recent work has in eukaryotes. However, there are additional dials that can
resultedindegradationtagsofvaryingsequencesthattune be used to tune genetic networks when working in
the rate of degradation by the ClpXP/AP degradation eukaryotes, as follows.
(a) (b) Inducer
N-Extein Intein C-Extein I
DNA
N–E C–E N–EC–E + I
Transcription
Inactive
mRNA
(c)
Translation +
la lb
protein folding
N–E C–E N–EC–E + lalb
I
N–E C–E (d) Inducer
Intein splicing la lb
N–EC–E + lalb
N–EC–E N–E C–E
Fig.5.Mechanismandtypesofinteins.Schematicrepresentationofthemechanismofproteinsplicing(a)anddifferentintein(I)
types.Smallmolecule(green)inducedsplicing(b),splitintein(Ia/Ib)splicing(c)andsmallmoleculeinducedsplitinteinsplicing
(d).
http://mic.sgmjournals.org 1245
