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Biology Blended with Math
and Computer Science
A. Malcolm Campbell
Reed CollegeMarch 7, 2008
DNA Microarrays:windows into a functional
genome
Opportunities for Undergraduate Research
How do microarrays work?
See the animation
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
Synthetic Biology
What is Synthetic Biology?
BioBrick Registry of Standard Parts
http://parts.mit.edu/registry/index.php/Main_Page
Peking University Imperial College
What is iGEM?
Davidson CollegeMalcolm Campbell (bio.)Laurie Heyer (math)Lance HardenSabriya Rosemond (HU)Samantha SimpsonErin Zwack
Missouri Western State U.Todd Eckdahl (bio.)Jeff Poet (math)Marian BroderickAdam BrownTrevor ButnerLane Heard (HS student)Eric JessenKelley MalloyBrad Ogden
SYNTHETIC BIOLOGYiGEM 2006
1234
Burnt Pancake Problem
Burnt Pancake Problem
Look familiar?
How to Make Flippable DNA Pancakes
hixCRBSRBS
hixC
TetTet
hixCpBad
pancake 1 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
hixCRBSRBS
hixC
TetTet
hixCpBad
pancake 1 pancake 2
TT TT
pLac
RBSRBS Hin LVAHin LVA
Hin Flips DNA of Different Sizes
Hin Flips Individual Segments
-2 1
No Equilibrium 11 hrs Post-transformation
Hin Flips Paired Segments
white light u.v.
mRFP off
mRFP on double-pancake flip
-2 1
2-1
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
Success at iGEM 2006
Time for another story?
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
1
3
5
4
2
1
3
5
4
2
The Hamiltonian Path Problem
Advantages of Bacterial Computation
Software Hardware Computation
Computation
Computation
Advantages of Bacterial Computation
Software Hardware Computation
Computation
Computation
$
¢
Cell Division
• Non-Polynomial (NP)
• No Efficient Algorithms
# of
Pro
cess
ors
Advantages of Bacterial Computation
3
Using Hin/hixC to Solve the HPPUsing Hin/hixC to Solve the
HPP
1 54 34 23 41 42 53 14
1
3
5
4
2
3
Using Hin/hixC to Solve the HPP
1 54 34 23 41 42 53 14
hixC Sites
1
3
5
4
2
Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
1
3
5
4
2
Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
1
3
5
4
2
Using Hin/hixC to Solve the HPP
Using Hin/hixC to Solve the HPP
1
3
5
4
2
Using Hin/hixC to Solve the HPP
Solved Hamiltonian Path
1
3
5
4
2
Using Hin/hixC to Solve the HPP
How to Split a Gene
RBSRBS
Promoter
ReporterReporter
hixCRBSRBS
Promoter
Repo- Repo- rter
Detectable Phenotype
Detectable Phenotype
?
Gene Splitter Software
Input Output
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.
http://gcat.davidson.edu/iGEM07/genesplitter.html
Gene-Splitter Output
Note: Oligos are optimized for Tm.
Predicting Outcomes of Bacterial Computation
Pro
babi
lity
of H
PP
Sol
utio
n
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) =
€
1−e−λ • λ x
x!x=0
k−1
∑
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
Probability of at least k solutions on m plasmids for a 14-edge graph
How Many Plasmids Do We Need?
False Positives
Extra Edge
1
3
5
4
2
PCR Fragment Length
PCR Fragment Length
1
3
5
4
2
False Positives
Detection of True Positives
0
0.25
0.5
0.75
1
4/6 6/9 7/12 7/14
# of
Tru
e P
ositi
ves
÷
Tot
al #
of P
ositi
ves
# of Nodes / # of Edges
1.00
10.00
100.00
1000.00
10000.00
100000.00
1000000.00
10000000.00
100000000.00
4/6 6/9 7/12 7/14
# of Nodes / # of Edges
Total # of Positives
# of Nodes / # of Edges
Tot
al #
of P
ositi
ves
Choosing Graphs
Graph 2
A
B
D
Graph 1
A
B
C
Splitting Reporter Genes
Green Fluorescent Protein Red 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
AC
A D
B
Graph 0
B
A
T7 RNAP
Hin + Unflipped
HPP
TransformationPCR to
Remove Hin
& Transform
Coupled Hin & HPP Graph
Flipping Detected by Phenotype
ACB
(Red)
BAC(None)
ABC(Yellow)
Hin-Mediated FlippingACB
(Red)
BAC(None)
ABC(Yellow)
Flipping Detected by Phenotype
ABC Flipping
Yellow
HinYellow, Green, Red, None
Red
ACB Flipping
HinYellow, Green, Red, None
BAC Flipping
Hin None
Yellow, Green, Red, None
Flipping Detected by PCR
ABC
ACB
BAC
Unflipped Flipped
ABCACB
BAC
Flipping Detected by PCR
ABC
ACB
BAC
Unflipped Flipped
ABCACB
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 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
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)hixpLac hix
