1. Undergraduates Learning Genomics Through Research A. Malcolm Campbell University of Wisconsin - Madison May 15, 2008

2. www.bio.davidson.edu/GCAT Seven Year Collaboration; Three Countries

3. GCAT Makes DNA Chips Affordable

4. Steady Growth Over Time 10,000 + Undergraduates and Counting

5. Distribution of GCAT Members

6. GCAT Publication of Outcomes Basic Research Publications 2008: 4 peer-reviewed publications 2007: 2 peer-reviewed publications 2006: 1 peer-reviewed publication

7. Student Learning Outcomes Question Topic Increase (%) 1. Microarray experimental error–dye bias + 36.2* 2. Microarray experimental error–gradient + 10.5* 3. Microarray negative controls + 10.3* 4. Microarray experimental design + 38.2* 5. Gene expression ratios using a graph+ 5.8* 6. Gene expression–probability + 0.2 7. Gene expression–gene clusters + 22.3* 8. Gene expression–regulatory cascade + 14.9* 9. Gene expression–gene circuit graphs + 11.8* 10. Interpreting microarray results + 19.0* 11. Diagnosis with microarrays + 12.5* * indicates p < 0.05; N = 409

8. Increased Student Interest in Research. Area Mean SD. Genomics 5.5 1.1 Biology 5.5 1.1 Research 5.4 1.2 1 = decreased a lot 7 = increased a lot N = 409

9. Student Satisfaction with Methods Activity Mean % >4 % >5 N Practicing data analysis before my own data 5.25 93.6 67.1 313 Isolating RNA or genomic DNA to produce probe 5.32 94.1 70.0 323 Producing the fluorescently labeled probe 5.22 94.4 68.9 306 Hybridizing the probe with the spotted DNA 5.20 92.8 70.1 334 Designing my own experiment 5.13 87.3 64.3 244 Analyzing data from public domain source 5.22 94.7 65.8 325 Reading papers that used DNA microarrays 5.06 88.9 62.4 343 1 = not effective at all 7 = highly effective

10. Course Grade Assessment Assessment method% Using method Test 42.2 Term paper/lab report 51.1 Poster presentation 33.3 Oral presentation 26.6 Manuscript for publication 8.8 Course evaluation 33.3 Informal feedback 62.2 Other 24.4

11. Mean SD Access to microarray technology without GCAT1.5 0.75 Online GCAT protocols useful4.4 0.69 The GCAT -Listserv helpful 4.2 1.0 GCAT network significant factor4.2 0.79 Positive experience using GCAT 4.6 0.60 I would use GCAT again in the future 4.7 0.63 Faculty Appreciate GCAT Resources 1 = strongly disagree 5 = strongly agree

12. “You have awakened parts of my brain that have been dormant since my last stats course. The only reason I have gone over the manual so carefully is that this is my first time teaching microarrays, or even using them, for that matter. GCAT has been remarkably helpful to me. In fact I don’t think I would have undertaken this new module in my lab course without the tools GCAT makes available.” Faculty Development

13. Introduced Microarrays Early Ben Kittinger ‘05 Wet-lab microarray simulation kit - fast, cheap, works every time.

14. GCAT Develops Commercial Product

15. Enable Students to Practice www.bio.davidson.edu/projects/GCAT/Spot_synthesizer/Spot_synthesizer.html

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

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

18. Comparative Genome Hybridizations

19. Synthetic Biology

20. What is Synthetic Biology?

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

22. Peking University Imperial College What is iGEM?

23. iGEM Team Mosaic of Institutions

24. Davidson College Malcolm Campbell (bio.) Laurie Heyer (math) Karmella Haynes (HHMI) 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

25. Advantages of Bacterial Computation SoftwareHardwareComputation

26. Advantages of Bacterial Computation SoftwareHardwareComputation $ ¢

27. 12341234 Burnt Pancake Problem

29. Look familiar?

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

32. Flipping DNA with Hin/hixC

35. 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

36. Hin Flips DNA of Different Sizes

37. Hin Flips Individual Segments -21

38. No Equilibrium 11 hrs Post-transformation

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

40. 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)

41. ( 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

42. ( 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

43. 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

44. Success at iGEM 2006

45. 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

46. The Hamiltonian Path Problem 1 3 5 4 2

47. 1 3 5 4 2

48. 3 Using Hin/hixC to Solve the HPP 1 54 342341425314 1 3 5 4 2

49. 3 1 54 342341425314 hixC Sites 1 3 5 4 2 Using Hin/hixC to Solve the HPP

50. 1 3 5 4 2

51. 1 3 5 4 2

52. 1 3 5 4 2

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

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

55. Gene Splitter Website 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

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

57. Predicting Outcomes of Bacterial Computation

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

59. 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?

60. False Positives Extra Edge 1 3 5 4 2

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

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

63. How to Build a Bacterial Computer

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

65. Splitting Reporter Genes Green Fluorescent ProteinRed Fluorescent Protein

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

67. 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

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

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

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

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

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

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

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

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

76. RFP1 hixC GFP2 BAC Flipping Detected by Sequencing

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

78. 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

79. 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.

80. What is the Focus?

81. Thanks to my life-long collaborators

83. Extra Slides

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

85. Enter: Flapjack & The Hotcakes

86. Wooly Mammoths of Missouri Western

87. Genome Sequencing

88. Sarah Elgin at Washington University Students finish and annotate genome sequences Support staff online Free workshops in St. Louis Growing number of schools participating Genome Education Partnership

89. Tuajuanda Jordan at HHMI Phage Genome Initiative Science Education Alliance Students isolate phage Students purify phage DNA; Sequenced at JGI Students annotate and compare geneomes National experiment to examine phage variation Free workshop and reagents

90. Cheryl Kerfeld at Joint Genome Institute > 1000 prokaryote genomes sequenced Students annotate genome Data posted online Workshop for training of faculty Wide range of species Undergraduate Genomics Research Initiative

91. Burnt Pancake Problem

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

93. 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 >>