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Missouri Western State University & Davidson College
Human Prac*ces
Two Synthe*c Biology Surveys 1. Faculty (high school & college/university)
– Familiarity and knowledge
– Currently teaching? – What support do you need?
2. General public – Create vs. Construct introduc*ons – Opinion of synthe*c biology – Should it be taught in schools?
Faculty Survey Results High School College/University
• 289 total responses
• Avg. teaching 15.5 years
• 88% teach at public schools
• 6% self‐assessed knowledgeable
• 0.7% deep knowledge
• 3.5% teaching it
• 1.4% on tests
• 192 total responses
• Avg. teaching 14.5 year
• 65% teach at public schools
• 12% self‐assessed knowledgeable
• 7% deep knowledge
• 6% teaching it
• 3% on tests
Faculty Survey Results High School
• 289 total responses
• Avg. teaching 15.5 years
• 88% teach at public schools
• 6% self‐assessed knowledgeable
• 0.7% deep knowledge
• 3.5% teaching it
• 1.4% on tests
• 192 total responses
• Avg. teaching 14.5 year
• 65% teach at public schools
• 12% self‐assessed knowledgeable
• 7% deep knowledge
• 6% teaching it
• 3% on tests
College/University
General Public Survey Results
Synthe*c biology uses molecular methods to create modified living organisms….
Synthe*c biology uses molecular methods to construct DNA‐based devices….
Measured “religiosity” for each person
General Public Survey Results
Create
Percep*on Scores for Each Manipula*on Percep
*on of synthe*
c biology
Construct
p < 0.008
+/‐ 95% conf.
General Public Survey Results
high religiosity
Create Manipula*on
Construct Manipula*on
Interac*on of Manipula*on and Religiosity Favorability towards synthe*
c biology
low religiosity
Defining the
Sa*sfiability Problem
The SAT Problem
The SAT Problem
The SAT Problem
The SAT Problem
The SAT Problem
Some “Key” Terms
Variable Clause
Input
Literal
Mathema*cal Transla*on
(G or B) and (G or b) and (G or ) and (g or )
What is the maximum number of clauses sa*sfied by any input?
Is there an input that sa*sfies all clauses?
SAT
Max SAT
History and Significance
NP Complete Problems
SAT (1971)
Hamiltonian Path Problem
Pancake Problem
Traveling Salesperson
Knapsack Problem
Optimal Linear
Arrangement
Minesweeper
Chromatic Number
0-1 Integer Programming
Applica*ons
• Electronics (e.g., equivalence checking) • Ar*ficial Intelligence (robot planning) • Bioinforma*cs (Haplotype inference)
Science Vol 288 19 May 2000
DNA Compu*ng Approach
Classifying SAT Problems
Coarse Distribu*ons
Fine Distribu*ons
doubledouble
0 1 1 double single
2 0 2 single single
2 3 1
Exactly 2 of 3 clauses sa*sfied, but differently
Biological Implementa*on
Central Dogma
DNA
mRNA
Protein
aug ccc uac uca cua ccu aua ccg cau
M P Y S H P I P H
transla*on
atgccctactcactacctatagcgcat
transcrip*on
Frameshii Muta*on
DNA
mRNA
Protein
aug ccc uac uca cua ccu aua ccg cau
M P Y S H P I P H
transcrip*on
transla*on
atgccctactcactacctatagcgcat DNA
mRNA
Protein
aug ccc UCu acu cac uac cua uac cgc au
atgcccTCtactcactacctatagcgcat
M P S T H Y H Y R
Frameshii Suppression
DNA
mRNA
Protein
aug ccc uac uca cua ccu aua ccg cau
M P Y S H P I P H
atgccctactcactacctatagcgcat DNA
Protein
aug cccUC uac uca cua ccu aua ccg cau
atgcccTCtactcactacctatagcgcat
M S Y S H P I P H
5 base suppressor tRNA
Physical Model of Suppressor tRNA
Milwaukee School of Engineering Center for Biomolecular Modeling
Custom Designed Educa*onal Tool
Coding 2‐SAT Logical Clause
11 base pairs
ATG + 5 bp + 1 bp + 5 bp
ATG NNNNN g NNNNN
1 logical clause
ATG NNNNN gNN NNN
ATG NNN NNg NNNNN
ATG NNN NNg NNN
Coding 2‐SAT Logical Clause
sa*sfied
ATG NNNNN gNN NNN
ATG NNN NNg NNNNN
ATG NNN NNg NNN
Coding 2‐SAT Logical Clause
sa*sfied
sa*sfied
OR
ATG NNNNN gNN NNN
ATG NNN NNg NNNNN
ATG NNN NNg NNN
Coding 2‐SAT Logical Clause
sa*sfied
sa*sfied
OR
no sa*sfac*on
In frame if, and only if, exactly 2 clauses sa*sfied.
Coding Mul*ple 2‐SAT Logical Clauses
11 bp
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4
3 + (6 x 11) + 2 + 2 = 73 bp
Coding Mul*ple 2‐SAT Logical Clauses
✓ ✓ ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
Coding Mul*ple 2‐SAT Logical Clauses
✓ ✓
✓ ✓ ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
Coding Mul*ple 2‐SAT Logical Clauses
✓ ✓
✓ ✓
✓
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 not sa*sfied
Coding Mul*ple 2‐SAT Logical Clauses
✓ ✓
✓ ✓
✓
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 not sa*sfied
ATG LC1 LC2 gg LC2 LC3 gg LC3 LC4 not sa*sfied
Constructed Eleven 5 base tRNAs
Anderson et al., 2002. Chemistry & Biology. 9: 237‐244
New Registry Parts
Constructed Eleven Frameshii Reporters
CCCTC
New Registry Parts
55 115
816
497
823
393
271
64
514 588
133 81
0
100
200
300
400
500
600
700
800
900
Occurrences of 5mers in Genome
Choice of Promoters
0
20
40
60
80
100
120
140
Normalized
RFP Fluorescence
Choice of Promoters
0
20
40
60
80
100
120
140
Normalized
RFP Fluorescence
Characterized Exis*ng Part
prone to spontaneous inser*ons
Suppressor tRNA Test Module v1.0 CCCTC
Suppressor tRNA Test Module v1.0 CCCTC
Biological Engineering Best Prac*ce
Construct
Design
Test
Redesign
Suppressor tRNA Test Module v2.0 CCCTC
pTet
Suppressor tRNA Test Module v2.0 CCCTC
pTet
Constructs % suppression Us Anderson
pBad-CCACU-pLac-RBS-CCACU-RFP 0.41 7.4
pTet-CCACU-pLac-RBS-CCACU-RFP 1.39 7.4
pLac-RBS-CCACU-RFP-pTet-CCACU 1.94 7.4
pBad-CGGUC-pLac-RBS-CGGUC-RFP 0.21 4.5
pTet-CGGUC-pLac-RBS-CGGUC-RFP 0.39 4.5
pLac-RBS-CGGUC-RFP-pTet-CGGUC 0.79 4.5
pBad-CCCUC-pLac-RBS-CCCUC-RFP 0.30 3.8
pTet-CCCUC-pLac-RBS-CCCUC-RFP 0.37 3.8
pLac-RBS-CCCUC-RFP-pTet-CCCUC 0.36 3.8
pBad-CCAUC-pLac-RBS-CCAUC-RFP 0.27 5.6
pBad-CUAGU-pLac-RBS-CUAGU-RFP 0.33 12.0
Amount of tRNA is Cri*cal
Suppressor tRNA Test Module v3.0
Constructs % suppression Us Anderson
pLac-RBS-CCACC-RFP-CCACC_tRNA 1.3 1.6
pLac-RBS-CCACC-RFP-CCAAU_tRNA 1.0 4.4
pLac-RBS-CCACC-RFP-CUACC_tRNA 0.9 7.4
CCACC
Automa*on Scale for Compu*ng
Human ParPcipaPon
Bacterial ParPcipaPon
Total Com
pu*n
g
Automa*on Level
One Literal per Clone
Lite
ral 1
Li
tera
l 2
a
a
a
b
b
b
a OR b a OR b’ b OR c’ a’ OR c
a
a a
b’
b’
b’
b
b
b
c’
c’
c’
a’
a’
a’
c
c
c
a’
b
c’
One Clause per Clone
a’
b
c’
a OR b a OR b’ b OR c’ a’ OR c
Solu*on to Max Sat = # of columns with posi*ve report
Mul*ple Clauses per Clone
a’
b
c’
Exactly 1 Exactly 2 Exactly 3 Exactly 4
(a OR b) AND (a OR b’) AND (b OR c’) AND (a’ OR c)
Solu*on to Max Sat
Max SAT in Popula*on
a’
b
c’
(a OR b) AND (a OR b’) AND (b OR c’) AND (a’ OR c)
Solu*on to Max Sat = Number of different reporters expressed
Max SAT in Each Clone
a’
b
c’
(a OR b) AND (a OR b’) AND (b OR c’) AND (a’ OR c)
Solu*on to Max Sat = Number of different reporters expressed
Bacterial Automa*on Scale
a’ b c’
(a OR b) AND (a OR b’) AND (b OR c’) AND (a’ OR c)
Automa*on Level
Acknowledgements • Chris Anderson UC Berkeley • Tim Herman and MSOE CBM • Kyri Bye‐Nagel Davidson College • Funding from
– NSF DMS 0733952 and 0733955 – HHMI grant #52006292 – Davidson DRI and Mar*n Genomics Program – Missouri Western Founda*on and Office of Academic and Student Affairs
Missouri Western State University & Davidson College
Exactly 2 out of 4 clauses sa*sfied
aug VVVVV gVV VVV XXX XXg XXX XXg gXX XXX gXX XXX YYYYY gYY YYY ggY YYY YgY YYY YZZ ZZZ gZZ ZZZ
aug VVVVV gVV VVV XXXXX gXX XXX ggX XXX XgX XXX XYY YYY gYY YYY ggY YYY YgY YYY YZZ ZZZ gZZ ZZZ
aug VVVVV gVV VVV XXX XXg XXX XXg gXX XXX gXX XXX YYY YYg YYY YYg gYY YYY gYY YYY ZZZZZ gZZ ZZZ
1 and 2
1 and 3
1 and 4