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

Seven Year Collaboration; Three Countries

GCAT Makes DNA Chips Affordable

Steady Growth Over Time 10,000 + Undergraduates and Counting

Distribution of GCAT Members

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

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

Increased Student Interest in Research. Area Mean SD. Genomics Biology Research = decreased a lot 7 = increased a lot N = 409

Student Satisfaction with Methods Activity Mean % >4 % >5 N Practicing data analysis before my own data Isolating RNA or genomic DNA to produce probe Producing the fluorescently labeled probe Hybridizing the probe with the spotted DNA Designing my own experiment Analyzing data from public domain source Reading papers that used DNA microarrays = not effective at all 7 = highly effective

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

Mean SD Access to microarray technology without GCAT Online GCAT protocols useful The GCAT -Listserv helpful GCAT network significant factor Positive experience using GCAT I would use GCAT again in the future Faculty Appreciate GCAT Resources 1 = strongly disagree 5 = strongly agree

“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

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

GCAT Develops Commercial Product

Enable Students to Practice

Open Source and Free Software

What Else Can Chips Do? Jackie Ryan ‘05

Comparative Genome Hybridizations

Synthetic Biology

What is Synthetic Biology?

BioBrick Registry of Standard Parts

Peking University Imperial College What is iGEM?

iGEM Team Mosaic of Institutions

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

Advantages of Bacterial Computation SoftwareHardwareComputation

Advantages of Bacterial Computation SoftwareHardwareComputation $ ¢

Burnt Pancake Problem

Look familiar?

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

Flipping DNA with Hin/hixC

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

Hin Flips DNA of Different Sizes

Hin Flips Individual Segments -21

No Equilibrium 11 hrs Post-transformation

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

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)

( 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

( 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

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

Success at iGEM 2006

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

The Hamiltonian Path Problem

3 Using Hin/hixC to Solve the HPP

hixC Sites Using Hin/hixC to Solve the HPP

Solved Hamiltonian Path Using Hin/hixC to Solve the HPP

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

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.

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

Predicting Outcomes of Bacterial Computation

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

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 = m = 10,000, ,000, ,000, ,000, ,000, ,000,000, Probability of at least k solutions on m plasmids for a 14-edge graph How Many Plasmids Do We Need?

False Positives Extra Edge

PCR Fragment Length False Positives

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

How to Build a Bacterial Computer

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

Splitting Reporter Genes Green Fluorescent ProteinRed Fluorescent Protein

GFP Split by hixC Splitting Reporter Genes RFP Split by hixC

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

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

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

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

ABC Flipping Yellow Hin Yellow, Green, Red, None

Red ACB Flipping Hin Yellow, Green, Red, None

BAC Flipping Hin None Yellow, Green, Red, None

Flipping Detected by PCR ABC ACB BAC UnflippedFlipped ABC ACB BAC

Flipping Detected by PCR ABC ACB BAC UnflippedFlipped ABC ACB BAC

RFP1 hixC GFP2 BAC Flipping Detected by Sequencing

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

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 , 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

Extra Slides

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

Enter: Flapjack & The Hotcakes

Wooly Mammoths of Missouri Western

Genome Sequencing

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

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

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

Burnt Pancake Problem

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

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 >>