1. Synthetic Biology Blends Math, Computer Science, and Biology A. Malcolm Campbell Reed College March 7, 2008

2. What is Synthetic Biology?

3. BioBrick Registry of Standard Parts http://parts.mit.edu/registry/index.php/Main_Page

4. Peking University Imperial College What is iGEM?

5. Davidson College Malcolm Campbell (bio.) Laurie Heyer (math) Lance Harden Sabriya Rosemond (HU) Samantha Simpson Erin Zwack Missouri Western State U. Todd Eckdahl (bio.) Jeff Poet (math) Marian Broderick Adam Brown Trevor Butner Lane Heard (HS student) Eric Jessen Kelley Malloy Brad Ogden SYNTHETIC BIOLOGY iGEM 2006

6. Enter: Flapjack & The Hotcakes Erin Zwack (Jr. Bio); Lance Harden (Soph. Math); Sabriya Rosemond (Jr. Bio)

7. Enter: Flapjack & The Hotcakes

8. Wooly Mammoths of Missouri Western

9. 12341234 Burnt Pancake Problem

12. Look familiar?

14. How to Make Flippable DNA Pancakes hixC RBS hixC Tet hixCpBad pancake 1pancake 2

15. Flipping DNA with Hin/hixC

18. How to Make Flippable DNA Pancakes All on 1 Plasmid: Two pancakes (Amp vector) + Hin hixC RBS hixC Tet hixCpBad pancake 1pancake 2 T T T T pLac RBS Hin LVA

19. Hin Flips DNA of Different Sizes

20. Hin Flips Individual Segments -21

21. No Equilibrium 11 hrs Post-transformation

22. Hin Flips Paired Segments white light u.v. mRFP off mRFP on double-pancake flip -2 1 2

23. Modeling to Understand Flipping ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) (1,2) (-1,2) (1,-2)(-1,-2) (-2,1)(-2,-1) (2,-1) (2,1)

24. ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) (1,2) (-1,2) (1,-2)(-1,-2) (-2,1)(-2,-1) (2,-1) (2,1) 1 flip: 0% solved Modeling to Understand Flipping

25. ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) (1,2) (-1,2) (1,-2)(-1,-2) (-2,1)(-2,-1) (2,-1) (2,1) 2 flips: 2/9 (22.2%) solved Modeling to Understand Flipping

26. PRACTICAL Proof-of-concept for bacterial computers Data storage n units gives 2 n (n!) combinations BASIC BIOLOGY RESEARCH Improved transgenes in vivo Evolutionary insights Consequences of DNA Flipping Devices -1,2 -2,-1 in 2 flips! gene regulator

27. Success at iGEM 2006

28. Living Hardware to Solve the Hamiltonian Path Problem, 2007 Faculty: Malcolm Campbell, Todd Eckdahl, Karmella Haynes, Laurie Heyer, Jeff Poet Students: Oyinade Adefuye, Will DeLoache, Jim Dickson, Andrew Martens, Amber Shoecraft, and Mike Waters; Jordan Baumgardner, Tom Crowley, Lane Heard, Nick Morton, Michelle Ritter, Jessica Treece, Matt Unzicker, Amanda Valencia

29. The Hamiltonian Path Problem 1 3 5 4 2

30. 1 3 5 4 2

31. Advantages of Bacterial Computation SoftwareHardwareComputation

32. Advantages of Bacterial Computation SoftwareHardwareComputation $ ¢

33. Cell Division Non-Polynomial (NP) No Efficient Algorithms # of Processors Advantages of Bacterial Computation

34. 3 Using Hin/hixC to Solve the HPP 1 54 342341425314 1 3 5 4 2

35. 3 1 54 342341425314 hixC Sites 1 3 5 4 2 Using Hin/hixC to Solve the HPP

36. 1 3 5 4 2

37. 1 3 5 4 2

38. 1 3 5 4 2

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

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

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

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

43. Predicting Outcomes of Bacterial Computation

44. Probability of HPP Solution Number of Flips 4 Nodes & 3 Edges Starting Arrangements

45. 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) = k = 151020 m = 10,000,000.0697000 50,000,000.3032.0000400 100,000,000.5145.000900 200,000,000.7643.0161.0000030 500,000,000.973.2961.00410 1,000,000,000.9992.8466.1932.00007 Probability of at least k solutions on m plasmids for a 14-edge graph How Many Plasmids Do We Need?

46. False Positives Extra Edge 1 3 5 4 2

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

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

49. How to Build a Bacterial Computer

50. Choosing Graphs Graph 2 A B D Graph 1 A B C

51. Splitting Reporter Genes Green Fluorescent ProteinRed Fluorescent Protein

52. GFP Split by hixC Splitting Reporter Genes RFP Split by hixC

53. HPP Constructs Graph 1 Constructs: Graph 2 Construct: AB ABC ACB BAC DBA Graph 0 Construct: Graph 2 Graph 1 B A C A D B Graph 0 B A

54. T7 RNAP Hin + Unflipped HPP Transformation PCR to Remove Hin & Transform Coupled Hin & HPP Graph

55. Flipping Detected by Phenotype ACB (Red) BAC (None) ABC (Yellow)

56. Hin-Mediated Flipping ACB (Red) BAC (None) ABC (Yellow) Flipping Detected by Phenotype

57. ABC Flipping Yellow Hin Yellow, Green, Red, None

58. Red ACB Flipping Hin Yellow, Green, Red, None

59. BAC Flipping Hin None Yellow, Green, Red, None

60. Flipping Detected by PCR ABC ACB BAC UnflippedFlipped ABC ACB BAC

61. Flipping Detected by PCR ABC ACB BAC UnflippedFlipped ABC ACB BAC

62. RFP1 hixC GFP2 BAC Flipping Detected by Sequencing

63. RFP1 hixC GFP2 RFP1 hixC RFP2 BAC Flipped-BAC Flipping Detected by Sequencing Hin

64. Conclusions Modeling revealed feasibility of our approach GFP and RFP successfully split using hixC Added 69 parts to the Registry HPP problems given to bacteria Flipping shown by fluorescence, PCR, and sequence Bacterial computers are working on the HPP and may have solved it

65. Living Hardware to Solve the Hamiltonian Path Problem Acknowledgements: Thanks to The Duke Endowment, HHMI, NSF DMS 0733955, Genome Consortium for Active Teaching, Davidson College James G. Martin Genomics Program, Missouri Western SGA, Foundation, and Summer Research Institute, and Karen Acker (DC ’07). Oyinade Adefuye is from North Carolina Central University and Amber Shoecraft is from Johnson C. Smith University.

66. What is the Focus?

67. Thanks to my life-long collaborators

69. DNA Microarrays: windows into a functional genome Opportunities for Undergraduate Research

70. How do microarrays work?

73. See Animation

74. Open Source and Free Software www.bio.davidson.edu/MAGIC

75. How Can Microarrays be Introduced? Ben Kittinger ‘05 Wet-lab microarray simulation kit - fast, cheap, works every time.

76. How Can Students Practice? www.bio.davidson.edu/projects/GCAT/Spot_synthesizer/Spot_synthesizer.html

77. What Else Can Chips Do? Jackie Ryan ‘05

78. Comparative Genome Hybridizations

79. Extra Slides

81. Can we build a biological computer? The burnt pancake problem can be modeled as DNA (-2, 4, -1, 3)(1, 2, 3, 4) DNA Computer Movie >>

82. Design of controlled flipping RBS-mRFP (reverse) hix RBS-tetA(C) hixpLachix