GCAT, Genome Sequencing, & Synthetic Biology A. Malcolm Campbell University of Washington March 5, 2008

How Can Microarrays be Introduced? Ben Kittinger ‘05 Wet-lab microarray simulation kit - fast, cheap, works every time.

How Can Students Practice?

Open Source and Free Software

What Else Can Chips Do? Jackie Ryan ‘05

Comparative Genome Hybridizations

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

Synthetic Biology

What is Synthetic Biology?

BioBrick Registry of Standard Parts

Peking University Imperial College What is iGEM?

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 SYNTHETIC BIOLOGY iGEM 2006

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

Burnt Pancake Problem

Look familiar?

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

Advantages of Bacterial Computation SoftwareHardwareComputation

Advantages of Bacterial Computation SoftwareHardwareComputation $ ¢

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

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 Software 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 & 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

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 (reverse) hix RBS-tetA(C) hixpLachix