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
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 2
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 3
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 4
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”. 5
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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 7
Feedback Analyze Parts Constraints Repository Repository short-dis.avi Workbench Clone M2S Evolutionary Algorithm Converter Implementation Synthesize Sequence 8
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’ 9
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 10
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 11
Synthesis and cloning Synthesis and cloning Newcastle device in pUC57 T4 DNA ligase Transform into E. coli pGFP-rrnB Integration Vector 7899bp ncl108 2200bp pGFP-rrnB pUC57-ncl08 8399bp 4908bp 10099bp pUC57 2708bp Ncl108 BBa_K104001 Bacillus integration vector 12
Genomic Integration Genomic Integration 13
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 14
Characterization of the peptide receiver peptide receiver Characterization of the device device 15
Cell Sorting Results Cell Sorting Results Subtilin Fluorescence 0% 7.70 1% 14.77 10% 21.95 16
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 17
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 18
Acknowledgements Acknowledgements • Our instructors: Our sponsors: – 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 – 19
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