Living Hardware to Solve the Hamiltonian Path Problem

1. Living Hardware to Solve the Hamiltonian Path Problem Faculty: Drs. Malcolm Campbell, Laurie Heyer, Karmella Haynes Faculty: Drs. Todd Eckdahl, Jeff Poet Students:Jordan Baumgardner, Tom Crowley, Lane H. Heard, Nickolaus Morton, Michelle Ritter, Jessica Treece, Matthew Unzicker, Amanda Valencia Students: Oyinade Adefuye, Will DeLoache, Jim Dickson, Andrew Martens, Amber Shoecraft, and Mike Waters

2. The Hamiltonian Path Problem 1 4 3 2 5

3. The Hamiltonian Path Problem 1 4 3 2 5

4. Advantages of Bacterial Computation Software Hardware Computation

5. Advantages of Bacterial Computation Software Hardware Computation Computation

6. http://www.turbosquid.com Advantages of Bacterial Computation Software Hardware Computation Computation Computation http://www.dnamnd.med.usyd.edu.au/

7. Non-Polynomial (NP) Computational Complexity • No Efficient Algorithms

8. Cell Division • Non-Polynomial (NP) Computational Complexity • No Efficient Algorithms

9. Flipping DNA with Hin/hixC

10. 1 4 3 2 5 Using Hin/hixC to Solve the HPP Using Hin/hixC to Solve the HPP

11. Using Hin/hixC to Solve the HPP 1 Using Hin/hixC to Solve the HPP 4 3 2 5 hixC Sites

12. Using Hin/hixC to Solve the HPP 1 Using Hin/hixC to Solve the HPP 4 3 2 5

13. Using Hin/hixC to Solve the HPP 1 Using Hin/hixC to Solve the HPP 4 3 2 5

14. 1 Using Hin/hixC to Solve the HPP 4 3 2 5 Solved Hamiltonian Path

15. How to Split a Gene Reporter Detectable Phenotype RBS Promoter

16. How to Split a Gene Reporter Detectable Phenotype RBS Promoter ? Detectable Phenotype Repo- rter RBS hixC Promoter

17. Gene Splitter Software http://gcat.davidson.edu/iGEM07/genesplitter.html Input Output Generates 4 Primers (optimized for Tm). 2. Biobrick ends are added to primers. 3. Frameshift is eliminated. 1. Gene Sequence (cut and paste) 2. Where do you want your hixC site? 3. Pick an extra base to avoid a frameshift.

18. Gene-Splitter Output Note: Oligos are optimized for Tm.

19. Can We Detect A Solution?

20. Starting Arrangement 4 Nodes & 3 Edges Probability of HPP Solution Number of Flips

21. True Positives 1 4 3 2 5 Elements in the shaded region can be arranged in any order. (Edges-Nodes+1) Number of True Positives = (Edges-(Nodes-1))! * 2

22. Probability of at least k solutions on m plasmids for a 14-edge graph How Many Plasmids Do We Need? k = actual number of occurrences λ= expected number of occurrences λ = m plasmids * # solved permutations of edges ÷ # permutations of edges Cumulative Poisson Distribution: P(# of solutions ≥ k) =

23. False Positives Extra Edge 1 4 3 2 5

24. False Positives PCR Fragment Length 1 4 3 2 5 PCR Fragment Length

25. Detection of True Positives Total # of Positives # of Nodes / # of Edges # of True Positives ÷ Total # of Positives # of Nodes / # of Edges

26. Building a Bacterial Computer

27. Splitting Reporter Genes Green Fluorescent Protein Red Fluorescent Protein

28. Splitting Reporter Genes Green Fluorescent Protein Red Fluorescent Protein

29. 3-Node Graphs Graph A Graph B

30. HPP Constructs Positive Control Construct: HPP0 Graph A Constructs: HPP-A0 HPP-A1 Graph A HPP-A2 Graph B Construct: HPP-B1 Graph B

31. Double Fluorescence HPP0 Green Fluorescence T7 RNAP

32. Double Fluorescence HPP0 Green Fluorescence T7 RNAP T7 RNAP HPP-A0 Yellow Fluorescence

33. Fluorometer Measurements GFP Excitation Spectra for 4 HPP Constructs (at an Emission Wavelength of 515nm) 450 nm chosen as excitation wavelength to measure GFP

34. Fluorometer Measurements RFP Excitation Spectra for 4 HPP Constructs(at an Emission Wavelength of 608nm) 560 nm chosen as excitation wavelength to measure RFP

35. Normalized Fluorometer Measurements

36. Flipping Detected by Phenotype HPP-A0 HPP-A1 HPP-A2

37. Flipping Detected by Phenotype HPP-A0 Hin-Mediated Flipping HPP-A1 HPP-A2

38. Flipping Detected by PCR HPP-A0 HPP-A1 HPP-A2 Unflipped Flipped

39. Pending Experiments • Test clonal colonies that contain flipped HPP and have the solution sequenced. • Perform a false-positive screen for HPP-B1 • Split 2 antibiotic resistance genes using a reading frame shift just after the RBS • Solve larger graphs • Solve the Traveling Salesperson Problem

40. Living Hardware to Solve the Hamiltonian Path Problem Thanks to: Karen Acker, Davidson College ‘07 Support: Davidson College Missouri Western State University The Duke Endowment HHMI NSF Genome Consortium for Active Teaching James G. Martin Genomics Program

41. Extra Slides

42. Traveling Salesperson Problem

43. Problem: Processivity • The nature of our construct requires a stable transcription mechanism that can read through multiple genes in vivo • Initiation Complex vs. Elongation Complex • Formal manipulation of gene expression (through promoter sequence and availability of accessory proteins) is out of the picture Solution : T7 bacteriophage RNA polymerase • Highly processive single subunit viral polymerase which maintains processivity in vivo and in vitro

44. Path at 3 nodes / 3 edges HP- 1 12 23 1 2 T 3

45. 2 1 T 4 3 Path at 4 nodes / 6 edges HP-1 12 24 43

46. 1 2 5 T 4 3 Path 5 nodes / 8 edges HP -1 12 25 54 43

47. 1 2 6 5 T 4 3 Path 6 nodes / 10 edgesHP-1 12 25 56 64 43

48. 1 2 6 5 T 4 3 7 Path 7 nodes / 12 edgesHP-1 12 25 56 67 74 43

49. Promoter Tester RBS:Kan:RBS:Tet:RBS:RFP Tested promoter-promoter tester-pSBIA7 on varying concentration plates • Used Promoter Tester-pSB1A7 and Promoter Tester-pSB1A2 without promoters as control • Further evidence that pSB1A7 isn’t completely insulated

50. Promoters Tested • Selected “strong” promoters that were also repressible from biobrick registry • ompC porin (BBa_R0082) • “Lambda phage”(BBa_R0051) • pLac (BBa_R0010) • Hybrid pLac (BBa_R0011) • None of the promoters “glowed red” • Rus (BBa_J3902) and CMV (BBa_J52034) not the parts that are listed in the registry