Table Of ContentRecent titles in the series
Volume 24 Techniques for the Study of Mycorrhiza
JR Norris, DJ Reed and AK Varma
Volume 25 Immunology of Infection
SHE Kaufmann and D Kabelitz
Volume 26 Yeast Gene Analysis
AJP Brown and MF Tuite
Volume 27 Bacterial Pathogenesis
P Williams, J Ketley and GPC Salmond
Volume 28 Automation
AG Craig and JD Hoheisel
Volume 29 Genetic Methods for Diverse Prokaryotes
MCM Smith and RE Sockett
Volume 30 Marine Microbiology
JH Paul
Volume 31 Molecular Cellular Microbiology
P Sansonetti and A Zychlinsky
Volume 32 Immunology of Infection, 2nd edition
SHE Kaufmann and D Kabelitz
Volume 33 Functional Microbial Genomics
B Wren and N Dorrell
Volume 34 Microbial Imaging
T Savidge and C Pothoulakis
Volume 35 Extremophiles
FA Rainey and A Oren
Volume 36 Yeast Gene Analysis, 2nd edition
I Stansfield and MJR Stark
Volume 37 Immunology of Infection
D Kabelitz and SHE Kaufmann
Volume 38 Taxonomy of Prokaryotes
Fred Rainey and Aharon Oren
Volume 39 Systems Biology of Bacteria
Colin Harwood and Anil Wipat
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Contributors
Jane Calvert
Science,TechnologyandInnovation Studies, University of Edinburgh,
Old Surgeons’Hall,High SchoolYards, Edinburgh, United Kingdom
Antoine Danchin
AMAbiotics, Evry, France; Department ofBiochemistry, Faculty ofMedicine,
The University ofHong Kong,Hong Kong,and CEA/Genoscope,Evry, France
Paul Freemont
Department ofLifeSciences, and Centre for SyntheticBiology and Innovation,
Imperial College London,London,United Kingdom
Emma Frow
Science,TechnologyandInnovation Studies, University of Edinburgh,
Old Surgeons’Hall,High SchoolYards, Edinburgh, United Kingdom
Lisa Goers
Department ofLifeSciences, and Centre for SyntheticBiology and Innovation,
Imperial College London,London,United Kingdom
JenniferS. Hallinan
SchoolofComputing Science,Newcastle University, Newcastle upon Tyne,
United Kingdom
Colin R. Harwood
CentreforSyntheticBiologyandBioexploitation,NewcastleUniversity,Newcastle
uponTyne,United Kingdom
Mikkel Girke Jørgensen
MolecularGeneticsGroup,GroningenBiomolecularSciencesandBiotechnology
Institute,Centre for SyntheticBiology, University of Groningen,Groningen,
The Netherlands
Nicolas Kylilis
Department ofLifeSciences, and Centre for SyntheticBiology and Innovation,
Imperial College London,London,United Kingdom
ChrisJ. Myers
DepartmentofElectricalandComputerEngineeringUniversityofUtah,SaltLake
City, Utah,USA
Susanne Pohl
CentreforSyntheticBiologyandBioexploitation,NewcastleUniversity,Newcastle
uponTyne,United Kingdom
Karen Polizzi
Department ofLifeSciences, and Centre for SyntheticBiology and Innovation,
Imperial College London,London,United Kingdom
xi
xii Contributors
Agnieszka Sekowska
AMAbiotics, Evry, France;Department ofBiochemistry,Faculty ofMedicine,
The University ofHong Kong,Hong Kong, and CEA/Genoscope, Evry, France
Wendy Smith
CentreforSyntheticBiologyandBioexploitation,NewcastleUniversity,Newcastle
upon Tyne, UnitedKingdom
Marios Tomazou
Department ofLife Sciences; Centre for SyntheticBiology and Innovation, and
Department ofBioengineering, Imperial CollegeLondon,London,United
Kingdom
Renskevan Raaphorst
MolecularGeneticsGroup,GroningenBiomolecularSciencesandBiotechnology
Institute, Centre for Synthetic Biology, University ofGroningen, Groningen,
The Netherlands
Jan-Willem Veening
MolecularGeneticsGroup,GroningenBiomolecularSciencesandBiotechnology
Institute, Centre for Synthetic Biology, University ofGroningen, Groningen,
The Netherlands
AnilWipat
CentreforSyntheticBiologyandBioexploitation,NewcastleUniversity,Newcastle
upon Tyne, UnitedKingdom
Ke YanWen
Department ofLife Sciences, and Centrefor Synthetic Biologyand Innovation,
Imperial CollegeLondon,London,UnitedKingdom
Preface
Microbial synthetic biology is a rapidly growing discipline that builds on well-
established principles of genetic engineering and biotechnology by integrating
computationalandengineeringapproachesintothedesignandconstructionofnovel
biologicalsystems.Althoughsyntheticbiologyrepresentsamultidisciplinarytech-
nologywithenormouspotentialtobenefitsociety,majorchallengesneedtobeover-
cometoachievearadicalstepchangeinourabilitytoengineercomplexmulti-scaled
biologicalsystems.Attheheartofthesechallengesisaneedtounderstandtheenor-
mouscomplexitythatunderlieseventhesimplestofbiologicalentities.Thisrequires
a detailed understanding of cell physiology, biochemistry and genetics, as well as
that of the sub-cellular molecular machinery that mediates essential biological
processes.
Thestepchangeintechnologythathasbroughtsyntheticbiologytotheforeisour
ability to analyse very large datasets generated by high-throughput omics technol-
ogy. The computational tools and approaches needed for the Computer-Aided
Design(CAD)andComputer-AidedManufacturing(CAM)ofcomplexmulti-scaled
biologicalsystems(andtheirconstituentmolecularmachines),althoughstillintheir
infancy,arealreadybeginningtoshowtheirvalue.Thechaptersinthisvolumehave
been chosen to address the methodology associated with different aspects of syn-
theticbiology.
Responsibleinnovationmustbeattheheartofthebioexploitationofbiological
systems. Calvert and Flow address the issue of responsible innovation and, in par-
ticular,theethical,legalandsocialimplicationsofsyntheticbiology.Theychallenge
someofthemorefamiliarwaysofaddressingtheseissuesbyproposingaseriesof
shifts in the framing and language used to discuss the social aspects of synthetic
biology.
TheChapterbyHallinandealswithcomputationalintelligenceandthedesignof
synthetic microbial systems. It addresses the issue of genome-scale circuit design,
oneoftheultimateaimsofsyntheticbiology.Criticaltothesuccessofsyntheticbiol-
ogyisthedevelopmentofplatformsfortheautomationofgeneticdesign.Myersout-
linesthestandardsneededfordatarepresentationthatfacilitatesgeneticdesigntasks
and discusses the future requirements for genome-scale automated genetic design
workflows.
Harwoodandcolleaguesfocusesononeofthemostwidelyusedsyntheticbiology
chassis,Bacillussubtilis.Thisbacteriumisalreadyusedextensivelyforthecommer-
cialproductionofproteins,enzymesandmetabolitessuchasriboflavin.Thischapter
discusses both the techniques used for genome refactoring and the computational
tools and resources required to model its metabolism. In their chapter, Danchin
and Sekowska address the constraints on the design of synthetic bacterial chassis,
contrasting the top-down and bottom-up approaches to chassis development. They
also remind the reader of the importance of time, the fourth dimension of any
three-dimensionalbiological system.
xiii
xiv Preface
Biological systems are noisy, and individual genetically identical cells in a
populationsubjectedtothesameenvironmentalconditionsexhibitavarietyofphe-
notypes. Girke and colleagues explore the origins, measurement and engineering
of noise and its influence on gene expression. As an example of the exploitation
ofsyntheticbiology,Goersandcolleaguesdiscussthedesign,engineeringandappli-
cationofbiosensors.Various typesofbiosensors aredescribedtogetherarange of
reportersusedtogenerateanobservablesignal.Thischapterdescribeshowmathe-
maticalmodelscanbeusedtopredictthecharacteristicsandperformanceofbiosen-
sors andaid their design.
We sincerely hope that this volume provides the reader with valuable insights
intothetechnologyandmethodscurrentlybeingdevelopedfortherapidlyexpanding
discipline ofsynthetic biology.
Colin Harwood and Anil Wipat
CHAPTER
1
Computational Intelligence
in the Design of Synthetic
Microbial Genetic Systems
JenniferS. Hallinan1
SchoolofComputingScience,NewcastleUniversity,NewcastleuponTyne,UnitedKingdom
1Correspondingauthor.e-mailaddress:[email protected]
1 INTRODUCTION
The oft-quoted defining characteristic of synthetic biology is “the application of
engineering principles to biological systems” (Andrianantoandro, Basu, Karig, &
Weiss,2006).Thisapproachisfundamentaltothediscipline.Engineeringconcepts
suchasabstraction,modularityandtheuseofstandardinterfacesareclearlyvaluable
foranydesignenterprise.Onasmallscale,engineeringapproachescanbeapplied
manuallytoindividualdesigns.Thetruepromiseofsyntheticbiologywill,however,
requirebiologicalengineeringonamuchlargerscale.Itisbecomingapparentthat
theoriginalaimofsyntheticbiology—thereductionofbiologicalpartsanddevices
totheequivalentofchildren’sbuildingblocks(Heinemann&Panke,2006)—isopti-
misticallyoversimplified;biologicalsystemsaremessy,stochasticandhighlyinter-
connected and do not lend themselves easily to division into tidy components
(Agapakis,Boyle, &Silver, 2012).
However,inordertorationallydesignmicrobialcellswithpredictable,desirable
functionality,somelevelofabstractionawayfromthiscomplexitymustbeachieved.
Smallcircuitscanbedesignedusingexpertknowledgeappliedtothemanipulation
of tiny, abstract modules such as BioBricks (Shetty, Endy, & Knight, 2008), as
demonstrated by hundreds of successful iGEM projects, and a plethora of ‘proof
of principle’ circuits implementing logic gates, inverters, oscillators and their ilk.
Practicallyusefulandcommerciallyviablesyntheticbiologywill,however,neces-
sarilyinvolvemuchlarger-scale designs.
Large-scale—even genome-scale—circuit design is often presented as an ulti-
mate aim of synthetic biology (Barrett, Kim, Kim, Palsson, & Lee, 2006; Carrera,
Rodrigo, & Jaramillo, 2009; Searls, Guan, Dunham, Caudy, & Troyanskaya,
2010; Esvelt & Wang, 2013). The reasons for this desire are twofold: for a start,
theabilitytocreateanentireorganism,nomatterhowcrippled,willprovideuswith
amuchmorecompleteunderstandingofthesystems-levelorganisation ofbacteria
thanwecurrentlypossess.Additionally,small-scaledesignsareinherentlylimitedin
the behaviours that they can produce, while larger-scale designs are theoretically
abletogeneratepracticallyanybiologicallyplausiblebehaviourenvisagedbytheir
1
MethodsinMicrobiology,Volume40,ISSN0580-9517,http://dx.doi.org/10.1016/B978-0-12-417029-2.00001-7
©2013ElsevierLtd.Allrightsreserved.
2 CHAPTER 1 Computational Intelligence
designers.Someprogresstowardsthisaimhasbeenmadebygroupssuchasthoseled
byCraigVenter(Gibsonetal.,2010)andGeorgeChurch(Forster&Church,2006;
Carr et al., 2012), but there is still a long way to go before this task is routine
(Noireaux,Maeda, &Libchaber, 2011).
Large-scaledesignswillhavetotakeintoaccountpracticalissuessuchasoptimal
codon usage, interactions between microbes and their environments and crosstalk
between existing and novel pathways within an organism. In order to maximise
the use of existing data and to identify, combine and evaluate biologically
meaningfulabstractionsofparts,devices,pathwaysandevengenomes,productive,
large-scalesyntheticbiologywillrequirecomputationaldataintegration,modelling,
simulation andanalysis.
2 COMPUTATIONAL INFRASTRUCTURE FOR SYNTHETIC
BIOLOGY
Computer science can contribute to synthetic biology in numerous ways, from the
establishmentofcommunity-basedstandards(Smithetal.,2007),throughthedevel-
opment of freely available Web-based resources (Ferna´ndez-Sua´rez & Galperin,
2013), to the building of automated eScience workflows (Craddock, Harwood,
Hallinan, & Wipat, 2008). Perhaps the most immediately valuable contributions,
however, are the application of automated data integration to the identification
and parameterisation of biological parts; automated circuit design; the simulation
of models of novel circuits; measurement of the behaviour of engineered circuits,
both in silico and in vivo; and automated computational reasoning for the analysis
ofexistingbehaviourandthegenerationoftestablehypothesesleadingtomodifica-
tionof models or the design ofnew experiments.
2.1 Biological parts
Conceptually,mostsyntheticbiologiststhinkintermsofparts(basicsequencecom-
ponentssuchaspromotersandcodingsequences)anddevices (collectionsofparts
thatworktogethertoproduceadesiredbehaviour,suchasasensororbistableswitch)
(Endy,2005;Canton,Labno,&Endy,2008).Inpractice,theconstructionofsynthetic
geneticcircuitshastraditionally,insofarasafieldasyoungassyntheticbiologymay
besaidtohavetraditions,beencarriedoutusingsequence-basedpartssuchasBio-
Bricks.BioBricksareshortDNAsequenceswithdefined50and30ends,designedto
enablerapidcombinationofdifferentBioBricksusingconventionalcloning,without
leavingascaronthesequence.EachBioBrickhasaclearlydefinedfunction,which
maybeassimpleasdetectingaspecificinputmoleculeorascomplexassensingmul-
tipleinputsandproducinganyofanumberofdesiredoutputs.Themajorrepositoryof
BioBricksistheRegistryofStandardBiologicalParts(Cantonetal.,2008).1
1http://partsregistry.org/Main_Page
2 Computational Infrastructure for Synthetic Biology 3
Syntheticbiologistsarenot,however,restrictedtodesignsusingcombinationsof
smallparts.TechnologiesforDNAsynthesisandsequencingaredevelopingrapidly.
The first gene synthesised, in 1965, was for an alanine–tRNA. Assembly of its 77
nucleotides took 5 years (Czar, Anderson, Bader, & Peccoud, 2009). At the time
ofwriting,DNAsynthesisiscommerciallyavailablefromaround$US0.28perbase
pair,andupto7.5millionbasepairscanbesynthesisedpermonth.2Theadventof
fast and relatively cheap synthesis means that the usefulness of sequence-based
approaches such as BioBricks is becoming somewhat moot. Novel circuits can be
designedintotoandsynthesisedrelativelyquicklyandcheaply.RapidDNAsynthe-
sis is convenient for the researcher and has several other advantages over conven-
tional cloningfor synthetic biology.
Amajoraimofmanysyntheticbiologyprojectsistheproductionoroverproduc-
tion of specific proteins or metabolites. The ability to combine protein-coding
sequencesfromavarietyoforganismsintoasinglecircuit,ortodesignentirelynovel
proteins,givesthesyntheticbiologistconsiderableflexibilityinthedesignofnovel
systems,buttherearesomecaveats.Differentorganismshavedifferentcodonusage
biases,meaningthatproteinsfromoneorganismcanbedifficulttoexpressinahet-
erologoushost(Gustafsson,Govindarajan,&Minshull,2004).Codonoptimisation
can lead tosignificant increasesinprotein expressionlevels.
Theuseoflarge-scaleDNAsynthesis,ratherthanconventionalcloning,means
thatsequence-levelmodificationssuchascodonusageoptimisationcanbedesigned
insilicoandimplementedefficiently.Inaddition,modificationstoabasicdesigncan
beproducedquicklyandcheaply.DNAsynthesiscompanieskeepcopiesofanorig-
inaldesign,andminorsequencemodifications(whichmayhavemajorphenotypic
effects)canbeproducedatconsiderablylesscostthanthatincurredbyacompletely
new sequence.
WhetherbycombiningBioBricksinthelaborbysendingdigitalsequencefilesto
asynthesiscompany,constructionofnovel syntheticgenetic circuitsiscurrently a
relatively straightforward, inexpensive process. However, before a circuit can be
constructed, it must bedesignedand the design evaluated.
2.2 Circuit design and simulation
2.2.1 Bottom-up versus top-down design
Thedesignapproachcurrentlydominantinsyntheticbiologyisessentiallymanual.
Onceaproblemisidentified,andanynecessaryrequirementsestablished,adomain
expertexaminespathwaysknownorsuspectedtobeinvolvedinthebiologicalfunc-
tionofinterest.Potentiallyusefulmodificationstoexistingpathwaysareidentified,
and a programme of laboratory experimentation is devised. This approach is
designated bottom-up design, because it starts with a consideration of the basic
components—partsanddevices—whichmaybeusedandbuildsthesystemsupfrom
there (Andrianantoandro et al., 2006; Schwille, 2011).
2http://www.genscript.com/gene_synthesis.html
4 CHAPTER 1 Computational Intelligence
This bottom-up design approach has a number of advantages. Perhaps most
importantly,itisintuitiveandreadilycomprehensibletothosewiththeappropriate
backgroundknowledge.Thelogicandreasoningbehindagivencircuitdesigncanbe
explained in detail. The behaviour of organisms carrying pathways designed in a
bottom-upmannermayberelativelystraightforwardtointerpret,sincethebehaviour
of the individual componentsisrelatively well understood.
However, the bottom-up approach also has a number of significant problems.
Most obvious is the need for detailed information, not only about individual parts
andtheirbehaviourbutalsoaboutthewaysinwhichtheyinteract.Evenforrelatively
well-studiedmodelorganisms,suchinformationissimplynotavailableforasignif-
icant proportion of genes. At the time of writing, the Genoscope database entry
(Barbe et al., 2009)3 for the important model Gram-positive bacterium Bacillus
subtilislists850(16%)ofits4533CDSashaving‘unknown’or‘hypothetical’func-
tion.Interactionsinvolvingtheseproteinsarealso,perforce,uncharacterised,asare
themajorityofotherbiophysicalinteractions,evenbetweenrelativelywell-studied
entities.Thesituationbecomesevenmoredifficultwhenoneconsidersthenumerous
RNAsandsubtlydifferentproteinstowhichposttranscriptionalmodificationsmay
give rise.
The number of potential interactions between a set of parts increases exponen-
tially with the size of the set. Consider a simple circuit consisting of a promoter,
aribosomalbindingsite(RBS),acodingsequence(CDS)andaterminator.Ifthere
aretwopossiblepromoters,twoRBSsandtwoterminatorsavailable(assumingthat
the CDS is an immutable readout), the possible number of circuits is
2(cid:2)2(cid:2)1(cid:2)2¼8. For three choices of each part, the number of combinations is
27; the number of possible combinations increases exponentially with the number
ofpartsavailable.Intherealworld,anumberofgroupshaveinvestigatedthegen-
erationandcharacterizationoflibrariesofpromoters(DeMey,Maertens,Lequeux,
Soetaert,&Vandamme,2007;Hartneretal.,2008;Blount,Weenink,Vasylechko,&
Ellis,2012),RBSs(Dougherty&Arnold,2009;Chen&Arkin,2012)andtermina-
tors(Temme,Hill,Segall-Shapiro,Moser,&Voigt,2012).Suchlibrariescanconsist
oftensorevenhundredsofindividualcomponents,mostofwhichareonlypartially
characterised.Predictingthebehaviourarisingfromtheinteractionsbetweenthese
componentsinalloftheirpotentialcombinationsissimplynotpossibleforanindi-
vidualmicrobiologist,nomatterhowexpert.Bottom-updesigns,withtheirdepen-
dence upon expert knowledge,are therefore necessarily limitedinsize.
Atamorefundamentallevel,bottom-updesignsarerestrictedtothosethatcanbe
conceivedof,andimplemented,byhumans.Thenumberofviable,productivebio-
logicalpathwaysproducedbytheevolutionaryprocessisatinyfractionofthosethat
potentiallyexistinthevastsolutionspacedefinedbyfourbases,20aminoacidsand
millionsofpossibleinteractionsbetweenbiomolecules.Giventhattheevolutionary
interests of naturally occurring microbes are not necessarily the same as those of
3https://www.genoscope.cns.fr/agc/microscope/mage/index.php?
