Synthetic Biology
Escherichia coli counter iGEM Summer 2004
Nathan Walsh
April 21, 2005
Acknowledgments
Boston University
Harvard University
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Will Blake
Jim Flanigon
Farren Isaacs
Ellen O’Shaughnessy
Neil Patel
Margot Schomp
Jim Collins
John Aach
Patrik D'haeseleer
Gary Gao
Jinkuk Kim
Xiaoxia Lin
Nathan Walsh
George Church
Thanks to:
Drew Endy & BioBricks community, MIT, Blue
Heron and all others who have supported us
along the way.
Overview
• Objectives & Design
• Testing Components
• Goals
• Conclusions and Next Steps
Objectives
Features/Design Constraints
• Ability to count identical inputs or sets of
identical inputs.
• Memory of the count recorded in the DNA of
current counter (and progeny).
• Modular bit design and linkage allows array of
n-bits to count up to 2n
• Exploit new class of natural mechanisms for use
in synthetic biology.
Objectives
Potential Applications
• Programmed cell death
– Safety
– Therapeutic dosage
• Environmental diagnostic
– Counting times pollution thresholds exceeded
• Metabolic diagnostic
– Count the number of times glucose levels
exceeded
Design
Phage Int/Xis system
Phage attachment sites
attP
P O P’
B O B’
attB
Bacterial attachment sites
Int
Int
+
Xis
Integrated Left attachment sites
Integrated Right attachment sites
attL
attR
B O P’
P O B’
Stably integrated prophage
Design
Phage Int/Xis system with inverted att sites
Phage attachment sites
Bacterial attachment sites
attP
attB*
P O P’
Int
B’ O B
Int
+
Xis
Integrated Right attachment site
Integrated Left attachment site
attR
attL*
P O B’
P’ O B
Design
Integrase advantages
•
High fidelity – site specific and directional recombination (as
opposed to homologous recombination)
•
Reversible – excision just as reliable as integration
•
Specific – each integrase recognize its own att sites, but no
others
•
Numerous – over 300 known Tyr integrases and ~30 known Ser
integrases
•
Efficient – very few other factors needed to integrate or excise
•
Extensively used – Phage systems well characterized and used
extensively in genetic engineering (e.g., the GATEWAY cloning
system by Invitrogen)
Groth et al., Phage Integrases: Biology and Applications, J. Mol. Biol., 335: 667-678)
Design
Full Cycle of Two ½-bits
State
Pulse
Products
0
Int2
Xis2
Rpt1
int
int22
xis2
rpt1
int2
xis2 reporter1
0
1A
Int2
0
1
attR
attP
attL
attB
**
attR11––term–– attL
11*1
term
1
1
Int1 Xis1
Rpt2
1
1B
Int2 Xis2
Rpt1
2B
Int1
1
0
0
0
2
Int1
Xis1
Rpt2
int1
xis1
rpt2
int1
xis1 reporter2
attP
attP
attR
–
–attB
attB
attL222***
22 –term–
2–term–
term
2A
Design
Chaining bits together
1
int2
xis2
TF3
2
int1
xis1
TF4
3
int4
xis4
TF5
4
int3
xis3
TF6
Components
Composite half bits in BioBricks
Two 2kb composite parts are currently being built
by Blue Heron:
λ Half Bit
BBa_I11060 :
p22 Half Bit
BBa_I11061 :
λ Int+
LVA
p22 attP
Reverse
Terminator
p22 attB
(rev comp)
BBa_I11020
BBa_I11033
BBa_B0025
BBa_I11032
p22 Int+
LVA
λ attP
Terminator
BBa_I11030
BBa_I11023
BBa_B0013
λ attB
(rev comp)
BBa_I11022
λ Xis
+AAV
ECFP
+AAV
BBa_I11021 BBa_E0024
P22 Xis
+AAV
EYFP
+AAV
BBa_I11031 BBa_E0034
Lewis and Hatfull, Nuc. Acid Res., 2001, Vol. 29, 2205-2216
Andersen, Applied and Environmental Microbiology, 1998, 2240-2246
Components
Lutz and Bujard Vector
Testing
Construct 1 - Overview
PLlacO
PLtetO
Xis
Int
Strain must make repressors
BU has used dh5aZ1 before
-laciq -> LacI
-PN25 -> TetR
T0
-endogenous araC
nalysis.txt
There are two sets of test plasmids,
one for lambda and one for P22
origin
Kan
attB*
attP
GFP_AAV
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Testing
Construct 1 – No GFP expression
PLlacO
PLtetO
dh5aZ1
Xis
Int
No GFP expression:
-Can’t continue after KanR
-Can’t read through attP
origin
Kan
attB*
attP
GFP_AAV
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Testing
Test Construct 2 – Might not be KanR problem
Para-1
PLtetO
dh5aZ1
attP
Int
GFP_AAV
GFP is not inducible
Likely problem is attP
attB*
origin
Kan
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Testing
Test Construct 3 – GFP alone works
Para-1
PLtetO
dh5aZ1
GFP_AAV
Int
GFP is produced
origin
Kan
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Testing
GFP is produced in the cells
Testing
Construct 1 – Possible explanations for failure
PLlacO
PLtetO
dh5aZ1
Xis
Int
Can’t read through attP
Cloning Problem near
PLlacO in lambda
construct (SalI)
Beginning of Int and
end of Xis overlap by
40 amino acids.
End of Int and attP
overlap.
origin
Kan
attB*
Can’t continue after KanR
attP
GFP_AAV
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Testing
Test Construct 1 – Fix
PLlacO
PLtetO
dh5aZ1
Xis
Int
Other Issues:
-Digests same size
-Reclone l Integrase
-Mutagenize attP site
-Swap attP and attB
-Have KanR-GFP intervening
sequence be coding
origin
Kan
attB*
attP
-Reduce excess space
GFP_AAV
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Goal
First bit counter
PLlacO
PLtetR
Lambda Int
p22 Int
p22 attB*
Lambda attB*
Lambda Xis
p22 Xis
Lambda attP
GFP_AAV
p22 attP
Kan
pSC101
Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210
Questions for Discussion
Please speak up with ideas!
• Is there enough Int?
• Do the PLlacO and PLtetO leak?
• How can we measure levels of Int/Xis?
• Does Int binding to att block read-through?
• What other constructs would be useful?
Synthesis and Testing
dh5aZ1 – and why we need a new strain
Try: OmniMAX2-T1 (invitrogen)
How Gateway does it
Gateway uses three methods
• Promoter – attB1 – rbs – gene of interest – attB2
• Promoter – rbs – Fusion – attB1 – gene of interest – attB2
• Promoter – attB1 – rbs – gene of interest – attB2 – Fusion
attB1 and attB2 can be read through with no stop codons but
the ribosome binding site (Shine Delgarno) must be
included after the attB1 if a native start is required
What we need to change
The Xis-attB-GFP junction
We want to make a protein across the junction
The GFP-attP-terminator
We want the attP and a transcriptional
terminator to follow the GFP
The next slides show P22 than lambda
P22Xis-P22attB-GFP junction
PLtetO rbs
xis
rbs
attB rbs
gfp
attP* t0
int*
F--T--M--S--*--*-M—R—K—G--H--D--K--L--I--T--Q--R--I--R--N--A--K--V--V--K--E--A--A--Y--A--*-attB
rbs
ttcatgacaagctaataacgcagcgcattcgtaatgcgaaggtcgttaaggaggcagcctatgcgtaagga
PLtetO: Lambda phage promoter with tet operator sites acting as repressive elements
rbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNA
attB/attB1: Phage P22 attachment site in host (capital letters are the Gateway l attB1)
xis: Phage P22 excisionase
int*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.
GFP-P22attP region
PLtetO rbs
xis
rbs
attB rbs
gfp
attP’ t0
int*
A--*--*-taataatttttggtacttctgtcccaaatatgtcccacagtaaaaataaggaaggcacgaataatacgt\
Aagtatttgatttaactggtgccgataataggagacgaacctacgaccttcgcattacgaattataagaact\
accttttaagtcaacaacataccacgtcatacctgcgctcacacgtcccatcttcgaaagacatgcaaagcc\
ttgcaaaccgatgcaaagatttgtatgtcccatttttgtcccaaaccacttag
Terminator
ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacg\
ctctcctgagtaggacaaatccgcc
attP: Phage integrase sites from phage P22
t0: Bacteriophage lambda transcriptional terminator
lXis-lattB-GFP junction
PLtetO rbs
l xis
rbs
l attB1
rbs
int*
gfp
l attP1’ t0
K--A--K--S--*--*-M—R—K—G-R--R--S--H—N—N—K—F—V—Q—K—S—R—L—R—R—Q—A--Y—A--*
attB1
rbs
AAGGCGAAGTCAtaataACAAGTTTGTACAAAAAAGCAGGCTaaggaggcaggcctatgcgtaagga
PLtetO: Lambda phage promoter with tet operator sites acting as repressive elements
rbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNA
attB1: Phage l attachment site attB1 from Gateway (BOB’)
xis: Phage P22 excisionase
int*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.
GFP-lattP region
PLtetO rbs
l xis
rbs
l attB1
rbs
int*
gfp
l attP1’ t0
A--*--*-taataacatagtgactggatatgttgtgttttacagtattatgtagtctgttttttatgcaaaatctaatt\
Taatatattgatatttatatcattttacgtttctcgttca(gcttttttgtacaaacttg)gcattataaaaaa\
gcattgctcatcaatttgttgcaacgaacaggtcactatcagtcaaaataaaatcattattt
Terminator
ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacgct\
ctcctgagtaggacaaatccgcc
attP: Phage integrase sites from phage l modified by Gateway (p’op)
t0: Bacteriophage lambda transcriptional terminator
0
Memory Element
DNA top half bit
Sequential D Flip-flop
Sequential D Flip-flops
using NOR gates
with separate clocks
Conditional Logic
to assure only one signal is passed
Int+Xis
IPTG
Int
Int
TET
Int alone
Int alone
Int+Xis
Memory Element
DNA bottom half bit
Conditional Logic
R-S flip-flop (NAND)
R
Circuits
R-S flip-flop (NOR)
R
Q
S
Q
S
SR Latch
Clocked R-S flip-flop (NOR)
R
Clocked D flip-flop (NOR)
D
Q
Q
CP
CP
S
D Flip-flop
Master Slave D flip-flop (NOR)
T flip-flop (NOR)
D
Q
CP
Negative Edge Triggered Flip-flop
Q
CP
Multi-University Collaboration
Boston University
Harvard University
• Ellen O’Shaughnessy
• Margot Schomp
• Jim Collins
•
•
•
•
•
•
John Aach
Farren Isaacs
Jinkuk Kim
Sasha Wait
Nathan Walsh
George Church
Simulation
Purpose
– To validate concept + alternatives, identify system
sensitivities
Implementation
– Mixed ODE / stochastic model using MatLab Simulink
– No uni-directional terminators
Level of Detail
– Pair of coupled half-bits
– Int and Xis mRNAs and proteins
– Half-bit DNA states
– IPTG and tet pulses
Parameters
– Mixture of literature values + model derived estimates
Results so far
– Stable switching depends on stability of Int vs. Xis
Simulation Results
Pulses: IPTG Tet
Seconds
1st half bit
DNA
mRNA: Int-Xis
Int
Protein:Int-Xis
Xis
Int
Seconds
2nd half bit
DNA
mRNA: Int-Xis
Int
Protein:Int-Xis
Xis
Int
Seconds
Simulation processing
• Initial configuration
IPTG
Int
half-bit 2
tet
Int
0
0
Xis
Xis
half-bit 1
0
0
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Simulation processing
• First IPTG pulse
Int protein
I
Xis protein
X
Int-Xis mRNA
IPTG
Int
half-bit 2
tet
Int
0
0
Xis
Xis
half-bit 1
0
I X
0
I X
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Int-Xis
Simulation processing
• First IPTG pulse
Int protein
I
Xis protein
X
Int-Xis mRNA
half-bit 1
IPTG
Int
half-bit 2
tet
Int
0
1
Xis
Xis
0
I X
1
I X
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Int-Xis
Simulation processing
• Post first IPTG pulse
half-bit 1
IPTG
Int
half-bit 2
tet
Int
0
1
Xis
Xis
0
1
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Simulation processing
• First tet pulse
half-bit 1
I X
IPTG
Int
0
I
Int protein
half-bit 2
tet
Int
0
1
Xis Xis protein
X
Int-Xis mRNA
Xis
1
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
I X
Int-Xis
Simulation processing
• First tet pulse
IPTG
Int
1
I
Int protein
half-bit 2
tet
Int
1
1
Xis
half-bit 1
I X
Xis protein
X
Int-Xis mRNA
Xis
1
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
I X
Int-Xis
Simulation processing
• Post first tet pulse
IPTG
Int
half-bit 2
tet
Int
1
1
Xis
half-bit 1
Xis
1
1
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Simulation processing
• Second IPTG pulse
half-bit 1
IPTG
Int
1
Int protein
Xis
I
Int mRNA
1
I
half-bit 2
tet
Int
1
Xis
1
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Simulation processing
• Second IPTG pulse
half-bit 1
IPTG
Int
1
Int protein
Xis
I
Int mRNA
1
I
tet
Int
0
Xis
half-bit 2
0
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Simulation processing
• Post second IPTG pulse
Int
half-bit 2
tet
Int
1
0
1
Xis
IPTG
Xis
half-bit 1
0
0
= integrated (attL / attR), requires Int+Xis to switch
1
= ‘excised’ (attP / attB), requires Int to switch
Model ODEs: example of basic structure
dmRNA Int - Xis 
V σ  
log(2)
 ε DNA  max 70   k δ RNAse  
dt
K m  σ 70  
τ
∆mRNAInt-Xis=
Amount
Synthesized
(DNA state)

  mRNA Int - Xis 

Amount
Amount
lost to cell
- Degraded division
(mRNAInt-Xis,
RNAseH*)
(mRNA)
• mRNA ODEs: 0 order generation 1st order
decay
• Generation / decay rates expressed as functions
of 70, RNAse concentrations, and doubling time
• Generation depends on variable DNA that
represents state of DNA
Model ODEs: additional details
• mRNA and protein stored as numbers of
molecules
• Int, Xis protein ODEs include Int-Xis complexing
as well as generation, decay, dilution
• Effect of transcript lengths on transcription and
translation taken into account via MatLab
“transport delays”
• Two sets of variables & equations one for each
half-bit
– 10 variables + 10 equations, not including DNA state
variables
• IPTG and tet: cycles of 4 parts of 1 hr 15min
– exposure to IPTG, recovery, exposed to Tet, recovery
Stochastic Modeling vs. ODEs
• DNA state switching not correctly modeled by
rate equation
dDNA 0 
 k s  [DNA1 ]  f([Int])  k d DNA 0 
dt
Wrong!!
• State switching modeled by change in
probability, not concentration
T
P(DNA1  DNA 0 | Int, T)  1  e
  f(Int(t))d t
T0
where
f(Int(t))t = probability of switch between t and t+t
Stochastic Modeling switching probability
f(X) = 1-(1-P)X
•
P = probability of integration or excision in time unit / molecule
– PInt = probability of integration / Int molecule
– PInt-Xis = probability of excision / Int-Xis complex
•
X = number of molecules of Int or Int-Xis
•
Additional constraint: X > Xmin
•
Implementation
– Pick random number U from uniform distribution 0..1
– If (X > Xmin) and U < f(X), invert DNA state
Matlab “Counter” Specific Models
• Protease and RNAse levels are constant
• The ProtInt and ProtInt-Xis output from one half bit are inputs for
other half bit
• The number of molecules are displayed on the “oscilliscopes”
Matlab: Molecular Biology Models
mRNA
protein
Matlab Molecular Biology Models
Complex between protein A and protein B
Matlab “Counter” Specific Models
Each half bit combines the switching function, the mRNA, and the protein.
The DNA state of each half bit is maintained as a global variable.
Matlab “Counter” Specific Models
The two half bits differ in that when they are in the integrated state
one makes mRNAInt and the other make mRNAInt-Xis.
Simulation Results – revisited
Pulses: IPTG Tet
Seconds
1st half bit
DNA
mRNA: Int-Xis
Int
Protein:Int-Xis
Xis
Int
Seconds
2nd half bit
DNA
mRNA: Int-Xis
Int
Protein:Int-Xis
Xis
Int
Seconds
Int/Xis degradation rates
The simulation is sensitive to the
relative degradation rates of Int
and Xis.
Previously Int was less stable,
but in this simulation the
stabilities are equal.
Simulation
Next steps and directions
• Continue evaluation of design elements
–
–
–
–
–
Explore more of parameter space
DNA element copy number
Reversible terminators
Single combined bits vs. coupled half-bits
Link multiple bits
• Incorporate more biology
– Continue refining parameters based on research
– Add additional molecules
• RNA polymerase, Ribosomes, competing DNA and RNA
– Model cell volume changes
– Model excision via Int / Xis / DNA interactions, not
Int+Xis complex
Considerations
• Phage systems
– Selection
• l, P22, HK022, P21 to start
• research + experiment to extend
– Cross-reactivity
– Multiple independent attP/attB per integrase
• E. coli strains
– Natural phage attB sites
– Recombination (use RecA-)
• Copy number
– F-plasmid?
• Speed of response
– Riboregulators?
• Gateway System intellectual property?
Conclusions
Next Steps
Conclusions
• Phage integrase systems useful for synthetic biology
• Integrase used to meet design objectives:
– DNA memory, counts same inputs, chainable
• Components are currently being constructed and tested
• ODE / stochastic simulator
Next Steps
• Continue with construction, testing of components
• Continue evaluating and refining designs with simulator
• Research, experimentation, and modifications to address
considerations
Acknowledgments
Boston University
Harvard University
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Will Blake
Jim Flanigon
Farren Isaacs
Ellen O’Shaughnessy
Neil Patel
Margot Schomp
Jim Collins
John Aach
Patrik D'haeseleer
Gary Gao
Jinkuk Kim
Xiaoxia Lin
Nathan Walsh
George Church
Thanks to:
Drew Endy & BioBricks community, MIT, Blue
Heron and all others who have supported us
along the way.
Design
Bit counter initial concept
0
Int1
00
1
0
Xis1
• Counting mechanism:
– Initial state:
0
– Pulse 1:
1
– Pulse 2:
0
– etc.
.
Int2
1
0
1
0
Xis2
Int2
Xis3
0 0
0 0
1 0
. .
• Race condition problems between each Int and Xis
Design
First Steps
1
Riboswitch
counter 
1
Int
Xis
TF4
2
Int
Xis
TF3
3
Int
Xis
TF5
4
Int
Xis
TF6
2
0
1
1
0
3
Integrase bit counter
Cell-cycle counter 
Definition
Finite state machine
A model of computation consisting of a set of
states, a start state, an input alphabet, and a
transition function that maps input symbols and
current states to a next state.
-National Institute of Standards and Technology