Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
BugBuster: Computational design of a bacterial biosensor 2008 Newcastle University iGEM team M. Aylward, R. Chalder, N. Ni...
Background <ul><li>Bacterial infection is a major cause of disease and death, particularly in developing countries </li></...
Sensing Bacteria <ul><li>Gram positive bacteria secrete ‘fingerprints’ of signal peptides, unique to the species or even t...
Choosing a Chassis <ul><li>Quorum sensing is well characterized in  Bacillus subtilis </li></ul><ul><li>Bacillus subtilis ...
The Challenge <ul><li>There are potentially many peptides to sense </li></ul><ul><li>Not just presence or absence, but als...
 
Meeting the Challenge <ul><li>Designing this kind of system by hand is not tractable </li></ul><ul><ul><li>Too many intera...
short- dis.avi Workbench M2S Converter Evolutionary Algorithm Parts Repository Constraints Repository Feedback Implementat...
Modelling with CellML <ul><li>Parts, and interactions between parts, have associated CellML models </li></ul><ul><li>CellM...
The peptide receiver device: design <ul><li>The wet-lab and the  in silico  parts of the project were proceeding in parall...
The peptide receiver device: implementation <ul><li>We designed a device by assembling multiple virtual parts </li></ul><u...
Synthesis and cloning pUC57-ncl08 4908bp 7899bp pUC57 2708bp ncl108 2200bp 10099bp Newcastle device in pUC57 Bacillus  int...
Genomic Integration
Characterizing BBa_K104001 <ul><li>Grow ATCC6633, and extract supernatant containing subtilin </li></ul><ul><li>Culture BB...
Characterization of the  peptide receiver device
Cell Sorting Results Subtilin Fluorescence 0% 7.70 1% 14.77 10% 21.95
Conclusions <ul><li>We have: </li></ul><ul><li>Demonstrated a bottom-up modelling approach for composing systems from smal...
Future work…if we had more time <ul><li>Characterize BBa_K104001 in more detail </li></ul><ul><li>Characterize other relev...
Acknowledgements <ul><li>Our instructors:  </li></ul><ul><ul><li>Dr. Jen Hallinan, School of Computing Science </li></ul><...
Upcoming SlideShare
Loading in …5
×
Upcoming SlideShare
Apogee
Next
Download to read offline and view in fullscreen.

0

Share

Download to read offline

Newcastle iGEM Presentation 2008

Download to read offline

Related Books

Free with a 30 day trial from Scribd

See all
  • Be the first to like this

Newcastle iGEM Presentation 2008

  1. 1. BugBuster: Computational design of a bacterial biosensor 2008 Newcastle University iGEM team M. Aylward, R. Chalder, N. Nielsen-Dzumhur, M. Taschuk , J. Thompson & M. Wappett
  2. 2. Background <ul><li>Bacterial infection is a major cause of disease and death, particularly in developing countries </li></ul><ul><li>Resistant strains are becoming a major problem </li></ul><ul><li>Quick, cheap and accurate diagnostics are invaluable </li></ul><ul><li>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 </li></ul>
  3. 3. Sensing Bacteria <ul><li>Gram positive bacteria secrete ‘fingerprints’ of signal peptides, unique to the species or even the strain </li></ul><ul><li>They also sense these peptides, to facilitate cell-cell communication within the strain </li></ul><ul><li>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 </li></ul><ul><li>Fluorescent proteins can provide a discriminatory output </li></ul>
  4. 4. Choosing a Chassis <ul><li>Quorum sensing is well characterized in Bacillus subtilis </li></ul><ul><li>Bacillus subtilis sporulates </li></ul><ul><ul><li>Spores are extremely resilient </li></ul></ul><ul><ul><li>Can be rehydrated as required </li></ul></ul><ul><li>Bacillis subtilis 168 is a well-characterized laboratory strain </li></ul><ul><ul><li>Genetically amenable </li></ul></ul><ul><ul><li>Competency can be induced </li></ul></ul><ul><li>Considerable expertise based in Newcastle in Cell and Molecular Biosciences </li></ul>
  5. 5. The Challenge <ul><li>There are potentially many peptides to sense </li></ul><ul><li>Not just presence or absence, but also relative levels of input </li></ul><ul><li>Only limited outputs possible </li></ul><ul><li>Want the choice of output to reflect the presence of pathogenic bacteria </li></ul><ul><li>This is a classical example of a multiplexing problem </li></ul><ul><li>A standard technique from computing science for addressing these kinds of problems is Artificial Neural Networks </li></ul><ul><li>The challenge: To implement an ANN in our bacterium, using genetic regulatory cascades to mimic the “neurons”. </li></ul>
  6. 7. Meeting the Challenge <ul><li>Designing this kind of system by hand is not tractable </li></ul><ul><ul><li>Too many interactions </li></ul></ul><ul><ul><li>Too many parameters to tune </li></ul></ul><ul><ul><li>Not enough time to ‘try it out’ in biology </li></ul></ul><ul><li>Computational approaches are required </li></ul><ul><ul><li>Evolutionary computing explores a large range of designs with many different interactions </li></ul></ul><ul><ul><li>Computational modelling of these designs evaluates the parameter space </li></ul></ul><ul><ul><li>Thousands of different designs with many parameterisations can be simulated before making even one engineered bacterium </li></ul></ul><ul><li>Computational solutions can then be implemented in vivo </li></ul><ul><li>Quantification of these biological constructs can feed back into the computational design process </li></ul>
  7. 8. short- dis.avi Workbench M2S Converter Evolutionary Algorithm Parts Repository Constraints Repository Feedback Implementation Sequence Synthesize Clone Analyze
  8. 9. Modelling with CellML <ul><li>Parts, and interactions between parts, have associated CellML models </li></ul><ul><li>CellML is modular. Each component: </li></ul><ul><ul><li>Captures the dynamic behaviour </li></ul></ul><ul><ul><li>Describes how it influences the behaviour of the parts it is attached to </li></ul></ul><ul><ul><li>Supports building complex, multi-component systems from small, modular descriptions – ‘bottom up’ modelling </li></ul></ul><ul><li>The Evolutionary Algorithm assembles models of the complete system from these part and interaction models </li></ul><ul><ul><li>Simulations predict the behaviour </li></ul></ul><ul><ul><li>Comparison to our specification to evaluate ‘fitness’ </li></ul></ul>
  9. 10. The peptide receiver device: design <ul><li>The wet-lab and the in silico parts of the project were proceeding in parallel </li></ul><ul><li>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 </li></ul><ul><li>This was modelled bottom up using CellML </li></ul>
  10. 11. The peptide receiver device: implementation <ul><li>We designed a device by assembling multiple virtual parts </li></ul><ul><li>The resulting DNA sequence (2.2k) was synthesized by GenScript Corporation </li></ul>
  11. 12. Synthesis and cloning pUC57-ncl08 4908bp 7899bp pUC57 2708bp ncl108 2200bp 10099bp Newcastle device in pUC57 Bacillus integration vector T4 DNA ligase Transform into E. coli Ncl108 BBa_K104001 pGFP-rrnB Integration Vector 8399bp pGFP-rrnB
  12. 13. Genomic Integration
  13. 14. Characterizing BBa_K104001 <ul><li>Grow ATCC6633, and extract supernatant containing subtilin </li></ul><ul><li>Culture BBa_K104001-transformed 168 in subtilin supernatant at concentrations of: </li></ul><ul><ul><li>0% </li></ul></ul><ul><ul><li>1% </li></ul></ul><ul><ul><li>10% </li></ul></ul><ul><li>Image under microscope </li></ul><ul><li>Quantify using Flow Cytometry </li></ul>
  14. 15. Characterization of the peptide receiver device
  15. 16. Cell Sorting Results Subtilin Fluorescence 0% 7.70 1% 14.77 10% 21.95
  16. 17. Conclusions <ul><li>We have: </li></ul><ul><li>Demonstrated a bottom-up modelling approach for composing systems from small functional modules, based upon CellML </li></ul><ul><li>Designed and implemented a software system for the computational design of complex regulatory networks </li></ul><ul><li>Successfully integrated a two-component quorum sensing system into Bacillus subtilis , demonstrating that our sensor approach is feasible </li></ul><ul><ul><li>Designed, modelled and submitted a working, standard BioBrick (BBa_K104001) for sensing the quorum communication peptide subtilin, that works as predicted </li></ul></ul><ul><li>Sent information and developed a B. subtilis website to help the Cambridge University team </li></ul><ul><li>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 </li></ul>
  17. 18. Future work…if we had more time <ul><li>Characterize BBa_K104001 in more detail </li></ul><ul><li>Characterize other relevant two-component quorum sensors, to expand the detection range and sensitivity </li></ul><ul><li>Implement and characterize the computationally-generated networks in vivo </li></ul><ul><li>Modify or replace the existing spaRK promoter to be constitutive, rather than linked to sporulation (SigA, not SigH) </li></ul><ul><li>Explore a wider range of output reporters </li></ul><ul><li>Produce the bacterium for use in the field </li></ul>
  18. 19. Acknowledgements <ul><li>Our instructors: </li></ul><ul><ul><li>Dr. Jen Hallinan, School of Computing Science </li></ul></ul><ul><ul><li>Dr. Matt Pocock, School of Computing Science </li></ul></ul><ul><ul><li>Prof. Anil Wipat, School of Computing Science </li></ul></ul><ul><li>Our advisors: </li></ul><ul><ul><li>Jan-Willem Veening, Institute for Cell and Molecular Bioscience </li></ul></ul><ul><ul><li>Leendert Hamoen, Institute for Cell and Molecular Bioscience </li></ul></ul><ul><ul><li>Colin Harwood, Institute for Cell and Molecular Bioscience </li></ul></ul><ul><ul><li>James Lawson, Auckland Bioengineering Institute </li></ul></ul><ul><ul><li>Michael T. Cooling, Auckland Bioengineering Institute </li></ul></ul><ul><ul><li>Glen Kemp, NEPAF </li></ul></ul><ul><ul><li>Achim Treuman, NEPAF </li></ul></ul>Our sponsors:

Views

Total views

578

On Slideshare

0

From embeds

0

Number of embeds

2

Actions

Downloads

5

Shares

0

Comments

0

Likes

0

×