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E.HOP: A Bacterial Computer to Solve the Pancake Problem

E.HOP: A Bacterial Computer to Solve the Pancake Problem. Davidson College iGEM Team Lance Harden, Sabriya Rosemond, Samantha Simpson, Erin Zwack. Faculty advisors: Malcolm Campbell, Karmella Haynes, Laurie Heyer. Basic Pancake Parts. Hin. Davidson Missouri Western. HinLVA. RBS. Kan.

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E.HOP: A Bacterial Computer to Solve the Pancake Problem

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  1. E.HOP: A Bacterial Computer to Solve the Pancake Problem Davidson College iGEM TeamLance Harden, Sabriya Rosemond, Samantha Simpson, Erin Zwack Faculty advisors: Malcolm Campbell, Karmella Haynes, Laurie Heyer

  2. Basic Pancake Parts Hin Davidson Missouri Western HinLVA RBS Kan Kan Chl Chl Tet Tet

  3. Basic Pancake Parts Hin Davidson Missouri Western HinLVA RBS Kan Kan Chl Chl Tet Tet

  4. T T T T Define a Biological Pancake Tet Nonfunctional RBS RE hixC pBad hixC hixC pancake 1 pancake 2 Tet Functional RBS RE hixC pBad hixC hixC pancake 1 pancake 2

  5. Modeling Pancake Flipping -2 4 -1 3 5C2 = 10 ways to choose two spatula positions Assume all 10 ways are equally likely Simulate random flipping process with MATLAB

  6. Modeling 8 Stacks of 2 Pancakes *Solution is (1, 2) or (-2, -1) ( 1 2) (-1 2) ( 1 -2) (-1 -2) ( 2 1) (-2 1) ( 2 -1) (-2 -1)

  7. Modeling 8 Stacks of 2 Pancakes *Solution is (1, 2) or (-2, -1) ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1)

  8. Modeling 8 Stacks of 2 Pancakes *Solution is (1, 2) or (-2, -1) ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1)

  9. Observed Tet Resistance + Tet RBS + Tet RBS + Tet RBS Tet RBS T T T T T T T T + Four One-Pancake Constructs PredictedTet Resistance (1) 2 + (-1) 2 - 1 (2) + 1 (-2) -

  10. Tet RBS Tet RBS Tet RBS Tet RBS T T T T T T T T How to Detect Flipping via Restriction Digest NheI NheI ExpectedFragment Size (1) 2 200 bp (-1) 2 300 bp 1 (2) 200 bp 1 (-2) 1100 bp

  11. Hin-mediated Flipping Tet Pancake Flipping pBad Pancake Flipping 1 (2) + Hin (1) 2 + Hin 1 (-2) (-1) 2 1 (2) (1) 2

  12. device EX SP T T pSB1A7 rep(pMB1) ampR T T T T Read-Through Transcription Blocked by pSB1A7 • Observed Tet Resistancein pSB1A3 • + (1) 2 • + (-1) 2 • + 1 (2) • + 1(-2)

  13. device EX SP T T pSB1A7 rep(pMB1) ampR T T T T Read-Through Transcription Blocked by pSB1A7 • Observed Tet Resistancein pSB1A3 • + (1) 2 • + (-1) 2 • + 1 (2) • + 1(-2) • Observed Tet Resistancein pSB1A7 • + (1) 2 • - (-1) 2 • + 1 (2) • - 1(-2)

  14. Hin LVA Tet AraC PC RBS RBS pLac hixC pBad hixC hixC RE T T T T Original Design Problems

  15. Unclonable Unclonable pSB1A7 Hin LVA Tet AraC PC RBS RBS pLac hixC pBad hixC hixC RE T T T T Original Design Problems

  16. Hin LVA Tet AraC PC RBS RBS pLac hixC pBad hixC hixC RE Flips too fast T T T T Original Design Problems

  17. T T T T Two-Plasmid Solution #1: Two pancakes (Amp vector) #2: AraC/Hin generator (Kan vector) Tet RBS pSB1A7 hixC pBad hixC hixC AraC PC Hin LVA RBS pSB1K3 pLac

  18. Tet RBS Tet RBS Biological Equivalence Problem (1,2) LB Amp + Kan+ Tet (-2,-1)

  19. Tet RBS RBS RFP RFP Tet RBS RBS Biological Equivalence Solution (1,2) (1,2) LB Amp + Kan+ Tet (-2,-1) (-2,-1)

  20. (-2,1) (-1,2) (-1,-2) (2,1) Eight Two-Pancake Stacks (1,2) (-2,-1) (2,-1) (1,-2)

  21. Intermediate Results Repress pBad-Tet Activate pLac-Hin Screen forOrientation

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

  23. Number of flips vs.Time? Number of flips Time Next Steps Better control of kinetics Slow down Hin activity Determine x flips second-1 Size bias?

  24. Modeling Plasmid Copy Number P(cell survives) = n = plasmid copy number p = probability of a pancake stack being sorted x = number of sorted stacks sufficient for cell survival

  25. Next Steps Improved insulating vector Accepts B0015 double terminator Bigger pancake stacks

  26. Summary: What We Learned Multiple campuses increase capacity with parallel processing Collective Troubleshooting Primarily Undergraduate Institutions can iGEM collaboratively

  27. Summary: What We Learned Math and Biology mesh really well Math modelingWe proved a new theorem! Challenges of biological components

  28. But above all… We had a flippin’ good time!!!

  29. E.HOP: A Bacterial Computer to Solve the Pancake Problem Collaborators at MWSU: Marian Broderick, Adam Brown, Trevor Butner, Lane Heard, Eric Jessen, Kelly Malloy, Brad Ogden Support: Davidson College HHMI NSF Genome Consortium for Active Teaching James G. Martin Genomics Program

  30. Extra Slides >>>

  31. Modeling 384 Stacks of 4 Pancakes 2n(n!) = X Combinations 24 (4!) = 384 Combinations

  32. Modeling 384 Stacks of 4 Pancakes * * Build one representative of each family * * * *

  33. Modeling Random Flipping for trial = 1 to n trials stack = input_stack flips = 0 while stack ~= 1:k flips = flips + 1 int = choose_random_interval stack = [stack(1:(int(1)-1)) -1 * fliplr(stack(int(1):int(2))) stack(int(2)+1:k)] end end

  34. -2 1 -2 -1 -1 2 1 2 1 -2 -1 -2 2 1 2 -1 P2: 2 Burnt Pancakes 8 vertices, each with degree 3 Flip length 1 Flip length 2

  35. -2 1 -2 -1 -1 2 1 2 1 -2 -1 -2 2 1 2 -1 P2: 2 Burnt Pancakes 8 vertices, each with degree 3

  36. -2 1 -2 -1 -1 2 1 2 1 -2 -1 -2 2 1 2 -1 B2: 2 Burnt Pancakes 8 vertices, each with degree 3

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