newcastle igem presentation 2008

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BugBuster: BugBuster: Computational design of a bacterial Computational design of a bacterial biosensor biosensor 2008 Newcastle University iGEM team M. Aylward, R. Chalder, N. Nielsen-Dzumhur, M. Taschuk , J. Thompson & M. Wappett

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Page 1: Newcastle iGEM Presentation 2008

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

Page 2: Newcastle iGEM Presentation 2008

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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

Page 3: Newcastle iGEM Presentation 2008

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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

Page 4: Newcastle iGEM Presentation 2008

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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

Page 8: Newcastle iGEM Presentation 2008

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short-dis.aviWorkbench

M2SConverter

EvolutionaryAlgorithm

PartsRepository

ConstraintsRepository

Sequence

Feedback

Synthesize

Clone

Analyze

Implementation

Page 9: Newcastle iGEM Presentation 2008

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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

Page 11: Newcastle iGEM Presentation 2008

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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

Page 12: Newcastle iGEM Presentation 2008

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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

Page 15: Newcastle iGEM Presentation 2008

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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

Page 19: Newcastle iGEM Presentation 2008

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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: