1 / 77

Biology Blended with Math and Computer Science

Biology Blended with Math and Computer Science. Malcolm Campbell. Reed College March 7, 2008. DNA Microarrays: windows into a functional genome Opportunities for Undergraduate Research. How do microarrays work?. See the animation. Open Source and Free Software.

saoirse
Download Presentation

Biology Blended with Math and Computer Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Biology Blended with Math and Computer Science 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. Ben Kittinger ‘05 How Can Microarrays be Introduced? 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. What is iGEM? Peking University Imperial College

  13. Davidson College Malcolm Campbell (bio.) Laurie Heyer (math) Lance Harden Sabriya Rosemond (HU) Samantha Simpson Erin Zwack SYNTHETIC BIOLOGY iGEM 2006 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

  14. Burnt Pancake Problem 1 2 3 4

  15. Burnt Pancake Problem

  16. Look familiar?

  17. How to Make Flippable DNA Pancakes Tet RBS hixC pBad hixC hixC pancake 1 pancake 2

  18. Flipping DNA with Hin/hixC

  19. Flipping DNA with Hin/hixC

  20. Flipping DNA with Hin/hixC

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

  22. Hin Flips DNA of Different Sizes

  23. Hin Flips Individual Segments -2 1

  24. No Equilibrium 11 hrs Post-transformation

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

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

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

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

  29. Success at iGEM 2006

  30. Time for another story?

  31. Living Hardware to Solve the Hamiltonian Path Problem, 2007 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 Faculty: Malcolm Campbell, Todd Eckdahl, Karmella Haynes, Laurie Heyer, Jeff Poet

  32. The Hamiltonian Path Problem 1 4 3 2 5

  33. The Hamiltonian Path Problem 1 4 3 2 5

  34. Advantages of Bacterial Computation Software Hardware Computation Computation Computation

  35. Advantages of Bacterial Computation Software Hardware Computation $ Computation ¢ Computation

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

  37. 1 4 3 2 5 Using Hin/hixC to Solve the HPP Using Hin/hixC to Solve the HPP 1 3 4 5 4 3 3 2 1 4 2 4 3 5 4 1

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

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

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

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

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

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

  44. Gene Splitter Software http://gcat.davidson.edu/iGEM07/genesplitter.html Input Output 1. 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.

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

  46. Predicting Outcomes of Bacterial Computation

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

  48. How Many Plasmids Do We Need? Probability of at least k solutions on m plasmids for a 14-edge graph 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) =

  49. False Positives Extra Edge 1 4 3 2 5

More Related