newcastle igem presentation 2008
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BugBuster:BugBuster:Computational design of a bacterial Computational design of a bacterial
biosensorbiosensor
2008 Newcastle University iGEM team
M. Aylward, R. Chalder, N. Nielsen-Dzumhur, M. Taschuk , J. Thompson & M. Wappett
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BackgroundBackground
• Bacterial infection is a major cause of disease and death, particularly in developing countries
• Resistant strains are becoming a major problem
• Quick, cheap and accurate diagnostics are invaluable
• We want to engineer a diagnostic tool to identify these infections, that can be used in situations where laboratory access, refrigeration and expensive chemicals are not available
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Sensing BacteriaSensing Bacteria
• Gram positive bacteria secrete ‘fingerprints’ of signal peptides, unique to the species or even the strain
• They also sense these peptides, to facilitate cell-cell communication within the strain
• We could potentially use the sensors for these peptides to design a bacterium which ‘works out’ what Gram positive bacteria are present in its environment
• Fluorescent proteins can provide a discriminatory output
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Choosing a ChassisChoosing a Chassis
• Quorum sensing is well characterized in Bacillus subtilis
• Bacillus subtilis sporulates– Spores are extremely resilient
– Can be rehydrated as required
• Bacillis subtilis 168 is a well-characterized laboratory strain
– Genetically amenable
– Competency can be induced
• Considerable expertise based in Newcastle in Cell and Molecular Biosciences
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The ChallengeThe Challenge
• There are potentially many peptides to sense
• Not just presence or absence, but also relative levels of input
• Only limited outputs possible
• Want the choice of output to reflect the presence of pathogenic bacteria
• This is a classical example of a multiplexing problem
• A standard technique from computing science for addressing these kinds of problems is Artificial Neural Networks
The challenge: To implement an ANN in our bacterium, using genetic regulatory cascades to mimic the “neurons”.
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Meeting the ChallengeMeeting the Challenge
• Designing this kind of system by hand is not tractable– Too many interactions
– Too many parameters to tune
– Not enough time to ‘try it out’ in biology
• Computational approaches are required– Evolutionary computing explores a large range of designs with many
different interactions
– Computational modelling of these designs evaluates the parameter space
– Thousands of different designs with many parameterisations can be simulated before making even one engineered bacterium
• Computational solutions can then be implemented in vivo
• Quantification of these biological constructs can feed back into the computational design process
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short-dis.aviWorkbench
M2SConverter
EvolutionaryAlgorithm
PartsRepository
ConstraintsRepository
Sequence
Feedback
Synthesize
Clone
Analyze
Implementation
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Modelling with CellMLModelling with CellML
• Parts, and interactions between parts, have associated CellML models
• CellML is modular. Each component:– Captures the dynamic behaviour – Describes how it influences the behaviour of the parts it is attached
to– Supports building complex, multi-component systems from small,
modular descriptions – ‘bottom up’ modelling
• The Evolutionary Algorithm assembles models of the complete system from these part and interaction models – Simulations predict the behaviour– Comparison to our specification to evaluate ‘fitness’
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The peptide receiver device: designThe peptide receiver device: design
• The wet-lab and the in silico parts of the project were proceeding in parallel
• We decided to build a peptide receiver device to test if our B. subtilis 168 was capable of sensing and responding to the subtilin quorum peptide (a lantibiotic) produced by B. subtilis ATCC6633
• This was modelled bottom up using CellML
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The peptide receiver device: The peptide receiver device: implementationimplementation
• We designed a device by assembling multiple virtual parts
• The resulting DNA sequence (2.2k) was synthesized by GenScript Corporation
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pUC57-ncl08
4908bp
Synthesis and cloningSynthesis and cloning
7899bp
pUC57
2708bp
ncl108
2200bp
10099bp
8399bp
pGFP-rrnB
Newcastle device in pUC57
Bacillus integration vector
T4 DNA ligaseTransform into E. coli
Ncl108
BBa_K104001
pGFP-rrnBIntegration Vector
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Genomic IntegrationGenomic Integration
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Characterizing BBa_K104001Characterizing BBa_K104001
• Grow ATCC6633, and extract supernatant containing subtilin
• Culture BBa_K104001-transformed 168 in subtilin supernatant at concentrations of:– 0%
– 1%
– 10%
• Image under microscope
• Quantify using Flow Cytometry
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Characterization of the Characterization of the peptide receiver peptide receiver devicedevice
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Cell Sorting ResultsCell Sorting Results
Subtilin Fluorescence0% 7.701% 14.7710% 21.95
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ConclusionsConclusions
We have:1. Demonstrated a bottom-up modelling approach for composing
systems from small functional modules, based upon CellML2. Designed and implemented a software system for the computational
design of complex regulatory networks3. Successfully integrated a two-component quorum sensing system into
Bacillus subtilis, demonstrating that our sensor approach is feasible– Designed, modelled and submitted a working, standard BioBrick
(BBa_K104001) for sensing the quorum communication peptide subtilin, that works as predicted
4. Sent information and developed a B. subtilis website to help the Cambridge University team
5. Taken the Cambridge 2007 BBa_I746107 AIP-inducible promoter P2 and GFP reporter, cloned it into an integration vector and successfully integrated it into the chromosome of 168, ready for further characterization
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Future work…if we had more timeFuture work…if we had more time
• Characterize BBa_K104001 in more detail
• Characterize other relevant two-component quorum sensors, to expand the detection range and sensitivity
• Implement and characterize the computationally-generated networks in vivo
• Modify or replace the existing spaRK promoter to be constitutive, rather than linked to sporulation (SigA, not SigH)
• Explore a wider range of output reporters
• Produce the bacterium for use in the field
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AcknowledgementsAcknowledgements
• Our instructors:
– Dr. Jen Hallinan, School of Computing Science– Dr. Matt Pocock, School of Computing Science– Prof. Anil Wipat, School of Computing Science
• Our advisors:
– Jan-Willem Veening, Institute for Cell and Molecular Bioscience
– Leendert Hamoen, Institute for Cell and Molecular Bioscience
– Colin Harwood, Institute for Cell and Molecular Bioscience
– James Lawson, Auckland Bioengineering Institute– Michael T. Cooling, Auckland Bioengineering
Institute– Glen Kemp, NEPAF– Achim Treuman, NEPAF
Our sponsors: