#### Transcript Undergraduate Research Opens Genomics Curriculum

Synthetic Biology Blends Math, Computer Science, and Biology A. Malcolm Campbell Reed College March 7, 2008 What is Synthetic Biology? BioBrick Registry of Standard Parts http://parts.mit.edu/registry/index.php/Main_Page What is iGEM? Peking University Imperial College SYNTHETIC BIOLOGY iGEM 2006 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 Enter: Flapjack & The Hotcakes Erin Zwack (Jr. Bio); Lance Harden (Soph. Math); Sabriya Rosemond (Jr. Bio) Enter: Flapjack & The Hotcakes Erin Zwack (Jr. Bio); Lance Harden (Soph. Math); Sabriya Rosemond (Jr. Bio) Wooly Mammoths of Missouri Western Burnt Pancake Problem 1 2 3 4 Burnt Pancake Problem Burnt Pancake Problem Look familiar? How to Make Flippable DNA Pancakes RBS hixC Tet pBad hixC pancake 1 hixC pancake 2 Flipping DNA with Hin/hixC Flipping DNA with Hin/hixC Flipping DNA with Hin/hixC How to Make Flippable DNA Pancakes All on 1 Plasmid: Two pancakes (Amp vector) + Hin RBS pLac Hin LVA T T RBS hixC Tet pBad hixC pancake 1 hixC pancake 2 Hin Flips DNA of Different Sizes Hin Flips Individual Segments -2 1 No Equilibrium 11 hrs Post-transformation Hin Flips Paired Segments mRFP off 1 -2 double-pancake flip mRFP on -1 white light 2 u.v. Modeling to Understand Flipping (-2,-1) (-2,1) (1,2) (-1,2) (1,-2) (-1,-2) (2,-1) (2,1) ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) Modeling to Understand Flipping (-2,-1) (-2,1) (1,2) (-1,2) (1,-2) (-1,-2) (2,-1) (2,1) 1 flip: 0% solved ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) Modeling to Understand Flipping (-2,-1) (-2,1) (1,2) (-1,2) (1,-2) (-1,-2) (2,-1) (2,1) 2 flips: 2/9 (22.2%) solved ( 1, 2) (-2, -1) ( 1, -2) (-1, 2) (-2, 1) ( 2, -1) (-1, -2) ( 2, 1) 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 gene Evolutionary insights Success at iGEM 2006 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 The Hamiltonian Path Problem 1 4 3 2 5 The Hamiltonian Path Problem 1 4 3 2 5 Advantages of Bacterial Computation Software Hardware Computation Computation Computation Advantages of Bacterial Computation Software Hardware Computation $ Computation ¢ Computation Advantages of Bacterial Computation • Non-Polynomial (NP) # of Processors • No Efficient Algorithms Cell Division Hin/hixC to to Solve the HPP Using Using Hin/hixC Solve the HPP 1 4 3 2 5 1 3 4 5 4 3 3 2 1 4 2 4 3 5 4 1 Hin/hixC to to Solve the HPP Using Using Hin/hixC Solve the HPP 1 4 3 2 5 1 3 4 5 4 3 3 2 1 4 2 4 hixC Sites 3 5 4 1 Hin/hixC to to Solve the HPP Using Using Hin/hixC Solve the HPP 1 4 3 2 5 Hin/hixC to to Solve the HPP Using Using Hin/hixC Solve the HPP 1 4 3 2 5 Using Hin/hixC Solvethe the HPP Using Hin/hixC to to Solve HPP 1 4 3 2 5 Using Hin/hixC to Solve the HPP 1 4 3 2 5 Solved Hamiltonian Path How to Split a Gene RBS Detectable Phenotype Reporter Promoter RBS Promoter Repo- rter hixC ? Detectable Phenotype Gene Splitter Software http://gcat.davidson.edu/iGEM07/genesplitter.html Input Output 1. Gene Sequence (cut and paste) 1. Generates 4 Primers (optimized for Tm). 2. Where do you want your hixC site? 2. Biobrick ends are added to primers. 3. Pick an extra base to avoid a frameshift. 3. Frameshift is eliminated. Gene-Splitter Output Note: Oligos are optimized for Tm. Predicting Outcomes of Bacterial Computation Probability of HPP Solution Starting Arrangements 4 Nodes & 3 Edges Number of Flips How Many Plasmids Do We Need? Probability of at least k solutions on m plasmids for a 14-edge graph k=1 5 10 20 m = 10,000,000 .0697 0 0 0 50,000,000 .3032 .00004 0 0 100,000,000 .5145 .0009 0 0 200,000,000 .7643 .0161 .000003 0 500,000,000 .973 .2961 .0041 0 1,000,000,000 .9992 .8466 .1932 .00007 k = actual number of occurrences λ = expected number of occurrences λ = m plasmids * # solved permutations of edges ÷ # permutations of edges Cumulative Poisson Distribution: e x P(# of solutions ≥ k) = 1 x! x0 k1 False Positives Extra Edge 1 4 3 2 5 False Positives PCR Fragment Length 1 4 3 2 5 PCR Fragment Length Detection of True Positives 100000000.00 # #ofofPositives Total Total Positives 10000000.00 1000000.00 100000.00 10000.00 1000.00 100.00 1 Total # of Positives # of True Positives ÷ 10.00 1.00 4/6 6/9 7/12 7/14 of Nodes / # of Edges ## of Nodes / # of Edges 0.75 0.5 0.25 0 4/6 6/9 7/12 # of Nodes / # of Edges 7/14 How to Build a Bacterial Computer Choosing Graphs C A A B B Graph 1 Graph 2 D Splitting Reporter Genes Green Fluorescent Protein Red Fluorescent Protein Splitting Reporter Genes GFP Split by hixC RFP Split by hixC HPP Constructs Graph 0 Construct: A AB B Graph 1 Constructs: Graph 0 ABC C ACB A B Graph 1 BAC Graph 2 Construct: DBA A B Graph 2 D Coupled Hin & HPP Graph Hin + Unflipped HPP Transformation PCR to Remove Hin & Transform Flipping Detected by Phenotype ABC (Yellow) ACB (Red) BAC (None) Flipping Detected by Phenotype ABC (Yellow) ACB (Red) BAC (None) Hin-Mediated Flipping ABC Flipping Yellow Hin Yellow, Green, Red, None ACB Flipping Red Hin Yellow, Green, Red, None BAC Flipping None Hin Yellow, Green, Red, None Flipping Detected by PCR ABC ACB BAC BAC ABC ACB Unflipped Flipped Flipping Detected by PCR ABC ACB BAC BAC ABC ACB Unflipped Flipped Flipping Detected by Sequencing BAC RFP1 hixC GFP2 Flipping Detected by Sequencing BAC RFP1 Flipped-BAC RFP1 hixC GFP2 Hin hixC RFP2 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 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. What is the Focus? Thanks to my life-long collaborators DNA Microarrays: windows into a functional genome Opportunities for Undergraduate Research How do microarrays work? How do microarrays work? How do microarrays work? How do microarrays work? See Animation Open Source and Free Software www.bio.davidson.edu/MAGIC How Can Microarrays be Introduced? Ben Kittinger ‘05 Wet-lab microarray simulation kit - fast, cheap, works every time. How Can Students Practice? www.bio.davidson.edu/projects/GCAT/Spot_synthesizer/Spot_synthesizer.html What Else Can Chips Do? Jackie Ryan ‘05 Comparative Genome Hybridizations Extra Slides 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 >> Design of controlled flipping RBS-mRFP hix pLac hix (reverse) RBS-tetA(C) hix