GCAT, Genome Sequencing,

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GCAT, Genome Sequencing, Synthetic Biology

1. Malcolm Campbell

University of Washington March 5, 2008
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www.bio.davidson.edu/GCAT
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How Can Microarrays be Introduced?
Wet-lab microarray simulation kit - fast, cheap,
works every time.
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How Can Students Practice?
www.bio.davidson.edu/projects/GCAT/Spot_synthesize
r/Spot_synthesizer.html
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Open Source and Free Software
www.bio.davidson.edu/MAGIC
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What Else Can Chips Do?
Jackie Ryan 05
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Comparative Genome Hybridizations
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Genome Sequencing
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Sarah Elgin at Washington University
Genome Education Partnership
Students finish and annotate genome
sequences Support staff online Free workshops
in St. Louis Growing number of schools
participating
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Tuajuanda Jordan at HHMI
Phage Genome InitiativeScience 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
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Cheryl Kerfeld at Joint Genome Institute
Undergraduate Genomics Research Initiative

• gt 1000 prokaryote genomes sequenced
• Students annotate genome
• Data posted online
• Workshop for training of faculty
• Wide range of species

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Synthetic Biology
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What is Synthetic Biology?
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BioBrick Registry of Standard Parts
http//parts.mit.edu/registry/index.php/Main_Page
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What is iGEM?
Peking University
Imperial College
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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
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Enter Flapjack The Hotcakes
Erin Zwack (Jr. Bio) Lance Harden (Soph. Math)
Sabriya Rosemond (Jr. Bio)
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Enter Flapjack The Hotcakes
Erin Zwack (Jr. Bio) Lance Harden (Soph. Math)
Sabriya Rosemond (Jr. Bio)
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Wooly Mammoths of Missouri Western
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Burnt Pancake Problem
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Burnt Pancake Problem
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Burnt Pancake Problem
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Look familiar?
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Flipping DNA with Hin/hixC
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Flipping DNA with Hin/hixC
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Flipping DNA with Hin/hixC
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How to Make Flippable DNA Pancakes
All on 1 Plasmid Two pancakes (Amp vector) Hin
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Hin Flips DNA of Different Sizes
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Hin Flips Individual Segments
-2
1
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No Equilibrium 11 hrs Post-transformation
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Hin Flips Paired Segments
mRFP off
1
-2
double-pancake flip
mRFP on
2
-1
u.v.
white light
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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)
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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
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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
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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
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Success at iGEM 2006
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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
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The Hamiltonian Path Problem
1
4
3
2
5
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The Hamiltonian Path Problem
1
4
3
2
5
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Advantages of Bacterial Computation
Software
Hardware
Computation
Computation
Computation
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Advantages of Bacterial Computation
Software
Hardware
Computation

Computation

Computation
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Advantages of Bacterial Computation

• Non-Polynomial (NP)

• No Efficient Algorithms

of Processors
Cell Division
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Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
3
1
5
4
3
4
2
3
4
1
4
2
5
3
1
4
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Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
3
1
5
4
3
4
2
3
4
1
4
2
5
3
1
4
hixC Sites
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Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
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Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
1
4
3
2
5
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Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
1
4
3
2
5
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Using Hin/hixC to Solve the HPP
1
4
3
2
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Solved Hamiltonian Path
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How to Split a Gene
Reporter
Detectable Phenotype
RBS
Promoter
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Detectable Phenotype
RBS
Repo-
rter
hixC
Promoter
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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.
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Gene-Splitter Output
Note Oligos are optimized for Tm.
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Predicting Outcomes of Bacterial Computation
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Starting Arrangements
4 Nodes 3 Edges
Probability of HPP Solution
Number of Flips
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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 ? expected
number of occurrences
? m plasmids solved permutations of edges
permutations of edges
Cumulative Poisson Distribution
P( of solutions k)
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False Positives
Extra Edge
1
4
3
2
5
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False Positives
PCR Fragment Length
1
4
3
2
5
PCR Fragment Length
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Detection of True Positives
Total of Positives
of Nodes / of Edges
of True Positives Total of Positives
of Nodes / of Edges
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How to Build a Bacterial Computer
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Choosing Graphs
D
A
B
Graph 2
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Splitting Reporter Genes
Green Fluorescent Protein
Red Fluorescent Protein
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Splitting Reporter Genes
GFP Split by hixC
RFP Split by hixC
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HPP Constructs
Graph 0 Construct
A
AB
B
Graph 0
Graph 1 Constructs
ABC
C
A
ACB
B
Graph 1
BAC
Graph 2 Construct
D
A
B
DBA
Graph 2
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Coupled Hin HPP Graph
PCR to Remove Hin Transform
Hin Unflipped HPP
Transformation
T7 RNAP
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Flipping Detected by Phenotype
ACB (Red)
BAC (None)
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Flipping Detected by Phenotype
Hin-Mediated Flipping
ACB (Red)
BAC (None)
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ABC Flipping
Yellow
Hin
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ACB Flipping
Red
Hin
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BAC Flipping
None
Hin
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Flipping Detected by PCR
ABC
ACB
BAC
BAC
ABC
ACB
Unflipped
Flipped
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Flipping Detected by PCR
ABC
ACB
BAC
BAC
ABC
ACB
Unflipped
Flipped
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Flipping Detected by Sequencing
BAC
RFP1 hixC
GFP2
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Flipping Detected by Sequencing
BAC
RFP1 hixC
GFP2
Hin
Flipped-BAC
RFP1 hixC
RFP2
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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

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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.
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What is the Focus?
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Thanks to my life-long collaborators
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Extra Slides
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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 gtgt
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Design of controlled flipping
RBS-mRFP (reverse)
hix
RBS-tetA(C)
hix
pLac
hix
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