BugBuster: BugBuster: Computational design of a bacterial - - PowerPoint PPT Presentation

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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Background Background

• 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 Bacteria Sensing 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 Chassis Choosing 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 Challenge The 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 Challenge Meeting 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.avi

Workbench

M2S Converter Evolutionary Algorithm Parts Repository Constraints Repository

Sequence Feedback Synthesize Clone Analyze Implementation

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Modelling with CellML Modelling 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: design The 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: implementation implementation

• 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 cloning Synthesis and cloning

7899bp pUC57 2708bp ncl108 2200bp 10099bp 8399bp pGFP-rrnB

Newcastle device in pUC57 Bacillus integration vector T4 DNA ligase Transform into E. coli

Ncl108 BBa_K104001 pGFP-rrnB Integration Vector

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Genomic Integration Genomic Integration

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Characterizing BBa_K104001 Characterizing 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 device device

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Cell Sorting Results Cell Sorting Results

Subtilin Fluorescence 0% 7.70 1% 14.77 10% 21.95

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Conclusions Conclusions

We have:

• Demonstrated a bottom-up modelling approach for composing

systems from small functional modules, based upon CellML

• Designed and implemented a software system for the computational

design of complex regulatory networks

• 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

• Sent information and developed a B. subtilis website to help the

Cambridge University team

• 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 time Future 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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Acknowledgements Acknowledgements

• 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: