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!
x0
k1
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