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

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

3. How do microarrays work? See the animation

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

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

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

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

8. Comparative Genome Hybridizations

9. Synthetic Biology

10. What is Synthetic Biology?

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

12. Peking University Imperial College What is iGEM?

13. 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

14. 12341234 Burnt Pancake Problem

16. Look familiar?

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

19. Flipping DNA with Hin/hixC

22. 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

23. Hin Flips DNA of Different Sizes

24. Hin Flips Individual Segments -21

25. No Equilibrium 11 hrs Post-transformation

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

27. 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)

28. ( 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

29. ( 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

30. Success at iGEM 2006

31. Time for another story?

32. 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

33. The Hamiltonian Path Problem 1 3 5 4 2

34. 1 3 5 4 2

35. Advantages of Bacterial Computation SoftwareHardwareComputation

36. Advantages of Bacterial Computation SoftwareHardwareComputation $ ¢

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

38. 3 Using Hin/hixC to Solve the HPP 1 54 342341425314 1 3 5 4 2

39. 3 1 54 342341425314 hixC Sites 1 3 5 4 2 Using Hin/hixC to Solve the HPP

40. 1 3 5 4 2

41. 1 3 5 4 2

42. 1 3 5 4 2

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

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

45. 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

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

47. Predicting Outcomes of Bacterial Computation

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

49. 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?

50. False Positives Extra Edge 1 3 5 4 2

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

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

53. How to Build a Bacterial Computer

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

55. Splitting Reporter Genes Green Fluorescent ProteinRed Fluorescent Protein

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

57. 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

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

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

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

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

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

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

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

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

66. RFP1 hixC GFP2 BAC Flipping Detected by Sequencing

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

68. 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

69. 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.

70. What is the Focus?

71. Thanks to my life-long collaborators

73. Extra Slides

75. 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 >>

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