synthetic biology escherichia coli counter igem summer 2004 nathan walsh april 21, 2005

Synthetic BiologyEscherichia coli counter iGEM Summer 2004Nathan WalshApril 21, 2005

Acknowledgments

Boston University• Will Blake• Jim Flanigon• Farren Isaacs• Ellen O’Shaughnessy• Neil Patel• Margot Schomp• Jim Collins

Harvard University• 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

ObjectivesFeatures/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.

ObjectivesPotential Applications

• Programmed cell death– Safety– Therapeutic dosage

• Environmental diagnostic– Counting times pollution thresholds

exceeded

• Metabolic diagnostic– Count the number of times glucose

levels exceeded

Phage attachment sites

attP

DesignPhage Int/Xis system

Int Int Xis+

attB Bacterial attachment sites

Integrated Left attachment sites

attLIntegrated Right attachment sites

attR

Stably integrated prophage

P’P O

B’B O

P’B O P O B’

DesignPhage Int/Xis system with inverted att sites

Int Int Xis

Phage attachment sites

attPBacterial attachment sites

attB*

+

P’P B’ BO O

Integrated Right attachment site

attRIntegrated Left attachment site

attL*P BP’B’O O

DesignIntegrase 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)

State

Pulse

Products

0

0

1A Int2

0

1

2AInt1 Xis1

Rpt2

1

1

1BInt2 Xis2

Rpt1

10

2B Int1

0

0

DesignFull Cycle of Two ½-bits

1 xis2 reporter1int2

2 xis1 reporter2int1

attR1 –term– attL1*

attP2 –term– attB2*

int2

Int2

int2

Int2

xis1 reporter2int1

attR2 – – attL2*

term

int1

Int1

xis1

Xis1

rpt2

Rpt2

xis1

Xis1

rpt2

Rpt2

int1

Int1

attP1 – – attB1*

xis2 reporter1int2

term

int2

Int2

xis2

Xis2

rpt1

Rpt1

xis1 reporter2int1

attP2 –term– attB2*

int2

Int2

xis2

Xis2

rpt1

Rpt1

int1

Int1

xis2 reporter1int2

attR1–term– attL1*

1 xis2 TF3int2

DesignChaining bits together

2 xis1 TF4int1

3 xis4 TF5int4

4 xis3 TF6int3

ComponentsComposite half bits in BioBricks

λ Xis +AAV

ECFP +AAV

λ Int+ LVA

BBa_E0024 BBa_I11020 BBa_I11021

p22 attP

BBa_I11033

Reverse Terminato

rBBa_B0025

p22 attB (rev

comp)BBa_I11032 BBa_I11060 :

P22 Xis

+AAV

EYFP +AAV

p22 Int+ LVA

BBa_E0034 BBa_I11030 BBa_I11031

λ attP

BBa_I11023

Terminator

BBa_B0013

λ attB (rev

comp)BBa_I11022 BBa_I11061 :

Lewis and Hatfull, Nuc. Acid Res., 2001, Vol. 29, 2205-2216Andersen, Applied and Environmental Microbiology, 1998, 2240-2246

Two 2kb composite parts are currently being built by Blue Heron:

λ Half Bit

p22 Half Bit

ComponentsLutz and Bujard Vector

TestingConstruct 1 - Overview

Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

Xis

Int

PLlacO PLtetO

GFP_AAVattP

attB*

origin

Kan

Strain must make repressorsBU has used dh5Z1 before-laciq -> LacI -PN25 -> TetR-endogenous araC

There are two sets of test plasmids,one for lambda and one for P22

T0

lambda_att_analysis.txt

TestingConstruct 1 – No GFP expression


Xis

Int

PLlacO PLtetO

GFP_AAVattP

attB*

origin

Kan

dh5Z1

No GFP expression:-Can’t continue after KanR-Can’t read through attP

TestingTest Construct 2 – Might not be KanR problem


Int

Para-1 PLtetO

GFP_AAV

attP

attB*

origin

Kan

dh5Z1

GFP is not inducibleLikely problem is attP

TestingTest Construct 3 – GFP alone works


Int

Para-1 PLtetO

GFP_AAV

origin

Kan

dh5Z1

GFP is produced

TestingGFP is produced in the cells

TestingConstruct 1 – Possible explanations for failure


Xis

Int

PLlacO PLtetO

GFP_AAVattP

attB*

origin

Kan

dh5Z1

Can’t read through attP

Beginning of Int andend of Xis overlap by 40 amino acids.

End of Int and attPoverlap.

Can’t continue after KanR

Cloning Problem near

PLlacO in lambda

construct (SalI)

TestingTest Construct 1 – Fix


Xis

Int

PLlacO PLtetO

attP

attB*

origin

Kan

dh5Z1

GFP_AAV

Other Issues:

-Digests same size

-Swap attP and attB-Have KanR-GFP intervening sequence be coding

-Mutagenize attP site

-Reclone Integrase

-Reduce excess space

GoalFirst bit counter


PLlacO

Lambda Int

p22 attP

p22 attB*

Lambda Xis

GFP_AAV

pSC101

Kan

p22 Xis

Lambda attB*

Lambda attP

p22 Int

PLtetR

Questions for DiscussionPlease 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 Testingdh5Z1 – 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 junctionWe want to make a protein across the junction

The GFP-attP-terminatorWe want the attP and a transcriptional terminator to follow the GFP

The next slides show P22 than lambda

P22Xis-P22attB-GFP junction

xis attBrbs gfp attP*rbsPLtetO

rbs 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--*--

ttcatgacaagctaataacgcagcgcattcgtaatgcgaaggtcgttaaggaggcagcctatgcgtaaggaattB rbs

t0

PLtetO: Lambda phage promoter with tet operator sites acting as repressive elementsrbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNAattB/attB1: Phage P22 attachment site in host (capital letters are the Gateway attB1)xis: Phage P22 excisionaseint*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.

GFP-P22attP region

xis attBrbs gfp attP’rbsPLtetO

rbs int*

t0

A--*--*-- taataatttttggtacttctgtcccaaatatgtcccacagtaaaaataaggaaggcacgaataatacgt\Aagtatttgatttaactggtgccgataataggagacgaacctacgaccttcgcattacgaattataagaact\accttttaagtcaacaacataccacgtcatacctgcgctcacacgtcccatcttcgaaagacatgcaaagcc\ttgcaaaccgatgcaaagatttgtatgtcccatttttgtcccaaaccacttagTerminatorggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacg\ctctcctgagtaggacaaatccgcc

attP: Phage integrase sites from phage P22t0: Bacteriophage lambda transcriptional terminator

Xis-attB-GFP junction

xis attB1rbs

gfp attP1’rbsPLtetO

rbs int*

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--*

AAGGCGAAGTCAtaataACAAGTTTGTACAAAAAAGCAGGCTaaggaggcaggcctatgcgtaaggaattB1 rbs

t0

PLtetO: Lambda phage promoter with tet operator sites acting as repressive elementsrbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNAattB1: Phage attachment site attB1 from Gateway (BOB’)xis: Phage P22 excisionaseint*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.

GFP-attP region

xis attB1rbs

gfp attP1’rbsPLtetO

rbs int*

t0

A--*--*-- taataacatagtgactggatatgttgtgttttacagtattatgtagtctgttttttatgcaaaatctaatt\Taatatattgatatttatatcattttacgtttctcgttca(gcttttttgtacaaacttg)gcattataaaaaa\gcattgctcatcaatttgttgcaacgaacaggtcactatcagtcaaaataaaatcattatttTerminatorggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacgct\ctcctgagtaggacaaatccgcc

attP: Phage integrase sites from phage modified by Gateway (p’op)t0: Bacteriophage lambda transcriptional terminator

Sequential D Flip-flop

Memory ElementDNA top half bit

Memory ElementDNA bottom half bit

Int alone

Int+Xis

Int alone

Int+Xis

IPTG

TET

Conditional Logicto assure only one signal is passed

Conditional Logic

Int

Int

Sequential D Flip-flopsusing NOR gates

with separate clocks

Circuits

R-S flip-flop (NOR)R-S flip-flop (NAND)

R

S

QR

S

Q

Clocked R-S flip-flop (NOR)

R

S

Q

CP

Clocked D flip-flop (NOR)

DQ

CP

T flip-flop (NOR)

CP

Q

Master Slave D flip-flop (NOR)

D

CP

Q

Negative Edge Triggered Flip-flop

D Flip-flop

SR Latch

Multi-University Collaboration

Boston University• Ellen O’Shaughnessy• Margot Schomp• Jim Collins

Harvard University• 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 TetTet

DNA

DNA

mRNA: Int-Xis IntProtein:Int-Xis Xis Int


2nd half bit

1st half bit

Seconds

Seconds

Seconds

Simulation processing

• Initial configuration

IPTG

0

Int Xis0 0

= integrated (attL / attR), requires Int+Xis to switch

tet 0 0Int

1= ‘excised’ (attP / attB), requires Int to switch

half-bit 1

half-bit 2

Xis


• First IPTG pulse

0= integrated (attL / attR), requires Int+Xis to switch

Int


Int-Xis mRNA

I XInt proteinXis protein

I X Int-Xis

I X

IPTG

Int Xis0 0

tet 0 0

half-bit 1

half-bit 2

Xis


• First IPTG pulse

IPTG

0

Int Xis0 0


tet 1 1Int


half-bit 1

half-bit 2Xis

Int-Xis mRNA

I XInt proteinXis protein

I X Int-Xis

I X


• Post first IPTG pulse

IPTG

0

Int Xis0 0


tet 1 1Int


half-bit 1

half-bit 2Xis


• First tet pulse

IPTG

0

Int Xis0 0


tet 1 1Int


half-bit 1

half-bit 2Xis

Int-Xis mRNAI XInt protein

Xis protein

I X

I X Int-Xis


• First tet pulse

IPTG

0

Int

Xis

1 1


tet 1 1Int


half-bit 1

half-bit 2Xis

Int-Xis mRNAI XInt protein

Xis protein

I X

I X Int-Xis


• Post first tet pulse

IPTG

0

Int

Xis

1 1


tet 1 1Int


half-bit 1

half-bit 2Xis


• Second IPTG pulse

IPTG

0

Int

Xis1 1


tet 1 1Int


half-bit 1

half-bit 2

Int mRNA IInt protein

I

Xis


• Second IPTG pulse

IPTG

0

Int

Xis1 1


tet 0 0Int


half-bit 1

half-bit 2

Int mRNA IInt protein

IXis


• Post second IPTG pulse

IPTG

0

Int

Xis1 1


tet 0 0Int


half-bit 1

half-bit 2

Xis

Model ODEs: example of basic structure

Xis-Intδ

70m

70maxDNA

Xis-Int mRNAτ

log(2)RNAsek

σK

σVε

dt

mRNAd

• 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

∆mRNAInt-Xis=Amount

Synthesized

(DNA state)

AmountDegraded(mRNAInt-Xis, RNAseH*)

- -Amount

lost to cell division(mRNA)

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

0d1s0 DNAkf([Int])][DNAk

dt

DNAd Wrong!!

• State switching modeled by change in probability, not concentration

T

0Ttf(Int(t))d

01 e1T)Int,|DNAP(DNA

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


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.


The two half bits differ in that when they are in the integrated stateone makes mRNAInt and the other make mRNAInt-Xis.

Simulation Results – revisited

Pulses: IPTG TetTet

DNA

DNA



2nd half bit

1st half bit

Seconds

Seconds

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.

SimulationNext 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

, 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?

ConclusionsNext 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• Will Blake• Jim Flanigon• Farren Isaacs• Ellen O’Shaughnessy• Neil Patel• Margot Schomp• Jim Collins

Harvard University• 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.

DesignBit counter initial concept

• Counting mechanism:– Initial state: 0 0 0– Pulse 1: 1 0 0– Pulse 2: 0 1 0– etc. . . .

• Race condition problems between each Int and Xis

Int1

0 0

Xis1 Int2 Xis2 Int2 Xis3

1 100

0

00

1

DesignFirst Steps

Xis TF4

Xis TF3

Int

Int

Xis TF5

Xis TF6

Int

Int

1

2

3

4

Riboswitch counter

Integrase bit counter

Cell-cycle counter

0110

1

2

3

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

synthetic biology escherichia coli counter igem summer 2004 nathan walsh april 21, 2005

Documents

term int

term attb

right attachment sites

left attachment sites

term attl

i11031 attp bba

pbp b oo slide

i11021 p22 attp bba