UPV-UV Valencia iGEM 2006

Alfonso JaramilloEcole Polytechnique, Paris

& InterTech (UPV)

Summary of work plan

• April: Training lectures, promotion, team building and funding.

• May/June: Prototype design.• July/August: Parts & Devices construction

& characterization. Redesign prototype.• September/October: System

characterization.

Synthetic BiologySynthetic Biology

Outline

• Biological modules for engineering– Parts– Devices– Systems

• Design of parts

Biological modules for engineering

• Chassis: Bacterial strain that will receive the engineered systems.

• Parts: Fragment of DNA with a given functionality.

• Devices: Assembly of parts with a given functionality and given interface.– Specifications– I/O is given by proteins and signals

• Systems: Assembly of devices with a given functionality– I/O is given only by signals

‘I need a few DNA binding proteins.’

‘Here’s a set of DNA binding proteins, 1N, that each recognize a unique cognate DNA site, choose any.’ ‘Get me this DNA.’

‘Here’s your DNA.’

‘Can I have three inverters?’

‘Here’s a set of PDP inverters, 1N, that each send and receive via a fungible signal carrier, PoPS.’

TAATACGACTCACTATAGGGAGA DNA

Zif268, Paveltich & Pabo c. 1991

Parts

PoPSNOT.1PoPS PoPS Devices

PoPS NOT.2

PoPS NOT.3

PoPS NOT.1

Systems

• Decoupling– Rules insulating design process from details of fabrication

– Enable parts, device, and system designers to work together

– VLSI electronics, 1970s

• Standardization– Predictable performance

– Off-the-shelf

– ME, 1800s

• Abstraction– Insulate relevant characteristics from overwhelming detail

– Simple artifacts that can be used in combination

– From Physics to EE, 1800s

Biological Engineering

Parts

• Promoter• RBS• CDS• Terminator• Tag• Primer• Operator

I13453 B0034 I15008 B0034 I15009 B0015tetR

R0040 B0034 I15010 B0015

BBa_M30109 =

Notice that for the MIT registry, any combination of parts (e.g. devices and systems) is a part.

Parts Construction

• Simplified cloning procedure that allows to combine plasmids using a standardized approach.

• The construction of devices is reduced to the combination of parts (to generate new parts, in the MIT terminology).

• Careful with the maximum size of plasmids

Parts Construction

S PXE

Standard PCR and sequencing primers have been chosen for pSB103 to use for colony PCR for insert length selection, and for sequencing of inserts:

Verication Forward: 5' TTG TCT CAT GAG CGG ATA CA 3‘Verication Reverse: 5' ATT ACC GCC TTT GAG TGA GC 3’.

cut the green plasmid with EcoRI and XbaI enzymes.

cut the blue plasmid with EcoRI and SpeI,

A minimum number of bases (undetermined) between the EcoRI and XbaI sites and the SpeI and PstI sites may be required to allow complete cutting with both enzymes (NotI).

The vector and insert must not contain any other EcoRI, XbaI, SpeI, or PstI cut sites. This excludesthe following hexamer sequences:

•EcoRI: GAATTC•XbaI: TCTAGA•SpeI: ACTAGT•PstI: CTGCAG

And octameric recognition site for NotI, GCGGCCGC.

Devices

• They should be able to always produce the same ouput from the same input.– Need of specification of transfer functions and I/O

proteins/molecules.– The engineer will be able to model the devices from the

specifications without needing to know the internals. Encapsulation of data.

– Devices with interface with each other. Need of a standard.

– In the real world the devices will interact with the chassis.

Devices

LacI CI inverter

CILacI

Devices

cI-857OLac RBS T

CILacI

LacI

CI

Systems

Inverter.2 Inverter.3Inverter.1

Device-Level System Diagram

Parts- and Device-Level System Diagram

DNA Layout

Device Interface

cI-857OLac RBS T

cILacI

cI-857RBS T

cI

O

PoPSin

PoPSout

LacI

cIPoPSout

PoPSin

cIRBST

O

cI

PoPSIN

Polymerase Per Second = PoPS!

cIRBST

O

PoPSOUT

cIRBST

O

PoPSOUT

PoPSIN

cI

PoPSOUTPoPSIN

Polymerase Per Second = PoPS!

INVERTER

PoPSOUTPoPSINPoPSOUT

PoPS Source (Any)

Towards an Ideal Chassis

• We would like to have a chassis that will not interfere with our devices.

• We need a dedicated protein production.

Biological Virtual Machines

• Dedicated systems are a method to decouple the function of an engineered biological system from the function of its chassis.

• By separating the resources and machinery used to supply and power an engineered system from those of the chassis, then perturbations in the operation of one should have less effect on the other.

• These dedicated synthesis systems can then be used as the basis of Biological virtual machines.

General Translation VM Translation

General Transcription

I7101

                   I7102

                     

VM Transcription

E7104

                     E7103

                    

The T7 expression system developed by Studier and coworkers is essentially orthogonal from the E. coli transcription system. T7 RNAP does not recognize E. coli promoters and E. coli RNAP does not recognize T7 promoters.

Design of Parts

Gene Designhttp://slam.bs.jhmi.edu/gd/

Zinc-finger repressor system

Designed linker sequence TGEKP between E3 and F4.

natural linker peptides between F1 and F2 (TGQKP) and between F2 and F3 (TGEKP),

Barbas PNAS 97bind DNA containing the 18-nt site 5'-GCGTGGGCGGCGTGGGCG-3'.

http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php

•Codon Usage: there is little tRNA made for rare codons. In E. coli these are mainly AGA and AGG (Arg codons). The genes encoding the tRNAs can be co-overexpressed.

•Promoters used should be very tight. Tight promoters are for example arabinose (BAD), rhamnose and lactose (provided that lacIQ is co-overexpressed).

•RBS (Shine-Delgarno Sequence - serves to align the ribosome on the message in the proper reading frame.) Optimal: AGG AGG, the last G should be 9 bases upstream from the A of the AUG (start of translation) Start codons on mRNA are: AUG (90%, codon for Met), GUG (9%), UUG (1%) and CUG (0.1%). Avoid secondary structure involving SD sequence and the initiator AUG. In polycistronic mRNAs, the initiation site should be close to the termination codon of the upstream gene.

•Regulation of translation: is sometimes (not often) affected by sequences in the coding region: +5 and +10 should be A or T

•RNA Stability can be increased if stem-loop structures are cloned at the 3' end of the coding region

Factors that Influence Gene Expression

Expression Vectors•Low copy number plamids are better than high copy number plasmids (copy numbers of 5-40 is recommended).

•Yield of protein does not linearly correlate with copy number of a gene.

•Regulated promoter - the optimum is one that is induced by a substrate such as IPTG that diffuses into the cell because then induction levels can be more easily manipulated.

•Inducers that are a substrate of one or more active transport systems cannot be controlled at all. These inducers will be accumulated to mM levels even when added in very low concentrations to the growth medium.

•Co-overexpression of repressors of the promoter used can provide excellent control of transcription. Co-overexpression of activators guarantees that each promoter on the plasmids is activated.

•Place transcription terminator at the 3' end of target gene. This avoids formation of antisense RNAs from downstream promoters operating in reverse orientation with respect to the gene that should be expressed at high levels.

Getting folded proteins

• Broken protein domains don't fold

• Difficult to get folded in the cytoplasm a protein containing disulfide bonds.

• Coexpress other members of a hetero-oligomeric complex

• Inclusion body formation can often be reduced by growing cells at 200C (Note that 280C does not work nearly as well). If this is not successful, try adding 6% ethanol to rich medium when adding inducer. EtOH induces heat shock response which overexpresses chaperones and proteases. The latter are barely active at 200C.

E.Coli parameters:http://redpoll.pharmacy.ualberta.ca/CCDB/cgi-bin/STAT_NEW.cgi

Parameters

DNA and RNA molecular weights:http://www.ambion.com/techlib/append/na_mw_tables.html

Design of Devices

Example of devices: Regulatory, input, output, sensors, signaling, metabolic, etc…

The biological inverter

Modeling a Biochemical Inverter

input

output

repressor

promoter

Steady-State Behavior: Transfer Functions

“ideal” transfer curve: gain (flat,steep,flat) adequate noise margins

[input]

“gain”

0 1

[output]

This curve can be achieved using proteins that cooperatively bind dna!

This curve can be achieved using proteins that cooperatively bind dna!

Inverter

Measuring a Transfer Curve

• Construct a circuit that allows:– Control and observation of input protein levels– Simultaneous observation of resulting output levels

“drive” gene output gene

R YFPCFP

inverter

• Also, need to normalize CFP vs YFP

Flow Cytometry (FACS)

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Fluorescence (FL1)

Eve

nts

Drive Input Levels by Varying InducerIPTG (uM)

0

250

1000

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Eve

nts

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Eve

nts

IPTGpINV-1024125 bp

Kan(r) lacI

EYFP

P(LAC)

P(lacIq)

p15A ori

T0 Term

T1 Term

(or ECFP)

plasmid

promoter

protein coding sequence

IPTG

YFP

lacI[high]

0(Off) P(LtetO-1)

P(R)

1.00

10.00

100.00

1,000.00

0.1 1.0 10.0 100.0 1,000.0 10,000.0

IPTG (uM)

FL

1 pINV-112-R1

pINV-102

Also use for yfp/cfp calibration

Controlling Input Levels

Measuring a Transfer Curve for lacI/p(lac)

EYFPlacIP(LAC)P(LtetO-1)

RBSIIRBSII

tetRLambda P(R-O12)

RBSII

aTc

ECFP

“drive”

output

aTc

YFPlacICFP

tetR[high]0

(Off) P(LtetO-1)

P(R)

P(lac)

measure TC

Transfer Curve Data Points

01 10

1 ng/ml aTc

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Fluorescence (FL1)

Eve

nts

undefined

10 ng/ml aTc 100 ng/ml aTc

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Fluorescence (FL1)

Eve

nts

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000 10,000

Fluorescence (FL1)

Eve

nts

1

10

100

1000

1 10 100 1000

Input (Normalized CFP)

Ou

tpu

t (Y

FP)

lacI/p(lac) Transfer Curve

aTc

YFPlacICFP

tetR[high]0

(Off) P(LtetO-1)

P(R)

P(lac)

gain = 4.72gain = 4.72

Evaluating the Transfer Curve• Noise margins:

0

200

400

600

800

1,000

1,200

1,400

1 10 100 1,000

Fluorescence

Eve

nts

30 ng/mlaTc

3 ng/mlaTc

1

10

100

1,000

0.1 1.0 10.0 100.0

aTc (ng/ml)

Flu

ore

scen

ce

• Gain / Signal restoration (95%):

high gainhigh gain

* note: graphing vs. aTc (i.e. transfer curve of 2 gates)

FACS cell population data

Logic Circuits based on Inverters

• Proteins are the wires/signals• Promoter + decay implement the gates• NAND gate is a universal logic element:

– any (finite) digital circuit can be built!

X

Y

R1 Z

R1

R1X

Y

Z= gene

gene

gene

NAND NOT

The IMPLIES Gate

• Inducers that inactivate repressors:– IPTG (Isopropylthio-ß-galactoside) Lac repressor

– aTc (Anhydrotetracycline) Tet repressor

• Use as a logical Implies gate: (NOT R) OR I

operatorpromoter gene

RNAP

activerepressor

operatorpromoter gene

RNAP

inactiverepressor

inducerno transcription transcription

Repressor Inducer Output

0 0 10 1 11 0 01 1 1

RepressorInducer

Output

10

1

102

103

100

101

102

100

101

102

103

IPTG (mM)

aTc (ng/ml)

Me

dia

n F

LR

Transfer Curve of Implies

YFPlacI

aTcIPTG

tetR[high]

Device Optimization

Measure cI/P(R) Inverter

OR1OR2 structural gene

P(R-O12)

• cI is a highly efficient repressor

cooperativebinding

IPTG

YFPcI

CFPlacI[high]0

(Off) P(R)P(lac)

• Use lacI/p(lac) as driver

highgain

cI bound to DNA

lacI CI+CFP YFP

IPTG

Initial Transfer Curve for cI/P(R)

• Completely flat– Reducing IPTG no additional fluorescence

• Hard to debug!

• Process engineering:Is there a mismatch between inverters based on

lacI/p(lac) and cI/P(R)?

1.00

10.00

100.00

1,000.00

0.1 1.0 10.0 100.0 1,000.0

IPTG (uM)O

utp

ut

(YF

P)

Inverters Rely onTranscription & Translation

mRNA

ribosome

promoter

mRNAribosome

operator

translation

transcription

RNAp

Process Engineering I:Different Ribosome Binding Sites

BioSpice Simulations

RBS

translation

start

Orig: ATTAAAGAGGAGAAATTAAGCATG strongRBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATGRBS-3: TCACACAGGACGGCCGGATG weak

1.00

10.00

100.00

1,000.00

0.1 1.0 10.0 100.0 1,000.0

IPTG (uM)

Ou

tpu

t (Y

FP

)

pINV-107/pINV-112-R1

pINV-107/pINV-112-R2

pINV-107/pINV-112-R3

Experimental Results forModified Inverter

Strong

Weak

Process Engineering II:Mutating the P(R)

orig: TACCTCTGGCGGTGATAmut4: TACATCTGGCGGTGATAmut5: TACATATGGCGGTGATAmut6 TACAGATGGCGGTGATA

 

OR1

Experimental Results for Mutating P(R)

1.00

10.00

100.00

1.000.00

0.1 1.0 10.0 100.0 1.000.0

IPTG (uM)

Ou

tpu

t (Y

FP

)

pINV-107-mut4/pINV-112-R3

pINV-107-mut5/pINV-112-R3

pINV-107-mut6/pINV-112-R3

Strong

Weak

Lessons for BioCircuit Design• Naive coupling of gates not likely to work• Need to understand “device physics”

– enables construction of complex circuits

• Use process engineering– modify gate characteristics

1.00

10.00

100.00

1,000.00

0.1 1.0 10.0 100.0 1,000.0

IPTG (uM)

Ou

tpu

t (Y

FP

)

RBS

RBS+O1

(a) Reducing the repressor/operator binding affinity

(b) Reducing the strength of the promoter

(c) Reducing the strength of the RBS

(d) Increasing the cistron count

(e) Adding autorepression

Other Gates

The Toggle Switch[Gardner & Collins, 2000]

pIKE = lac/tetpTAK = lac/cIts

Actual Behavior of Toggle Switch[Gardner & Collins, 2000]

promoter

protein coding sequence

Example of Devices

Input

• Membrane proteins (including ion channels)• Sensors (some of them membrane prots)

Sensors

• Using inducible promoters– IPTG, tetracycline, etc...– Temperature (sigma32)

• Using RNA• Design custom sensors

– Light detector (using phytochrome)

Genetic light switch• Phytochrome suffers a (reversible) conformational change

upon red light induction.• Tested in Yeast, but it can be implanted in any organism

able to synthesize the chromophore and assemble it.

Shimizu-Sato et al. Nat. Biotech. 2002

Chromophore Biosynthesis

Gambetta et al. PNAS 2001

Output

• Reporters– GFP, RFP, YFP– LacZ

GFP

• Highly resistant to temperature, pH, chemical denaturants, and proteases

• Intrinsic and independent fluorescence allows in vivo monitoring

• Easily measured by UV light, fluorescence microscopy, or FACS

• GFP fusion proteins retain biological activity (N and C-terminal)

• Applicable to many systems (E. coli, Drosophila, mammalian)

BBa_E0040

LacZ

• By addition of S-gal (3,4-cyclohexenoesculetin-D-galactopyranoside), LacZ catalyses the formation of a stable, insoluble,black precipitate from S-gal.

BBa_E0033

Signaling Devices

Signaling

• Senders• Receivers• Transmitters• Amplifiers

Intercellular Communications• Certain inducers useful for communications:

1. A cell produces inducer2. Inducer diffuses outside the cell3. Inducer enters another cell4. Inducer interacts with repressor/activator change signal

(1) (2) (3) (4)

mainmetabolism

Quorum Sensing

• Cell density dependent gene expression

Example: Vibrio fischeri [density dependent bioluminscence]

The lux Operon LuxI metabolism autoinducer (VAI)

luxR luxI luxC luxD luxA luxB luxE luxG

LuxR LuxI(Light)

hv(Light)

hvLuciferaseLuciferase

P

P

Regulatory Genes Structural Genes

Light organ

Eupryma scolopes

Density Dependent Bioluminescence

free living, 10 cells/liter<0.8 photons/second/cell

symbiotic, 1010 cells/liter 800 photons/second/cell

A positive feedback circuit

luxR luxI luxC luxD luxA luxB luxE luxG

LuxRLuxI

P

P

Low Cell DensityLow Cell Density

luxR luxI luxC luxD luxA luxB luxE luxG

LuxR LuxI

(Light)hv

(Light)hvLuciferaseLuciferase

P

P

High Cell DensityHigh Cell Density

LuxRO O

O

ONH

O OO

ONH

O OO

ONH

O OO

ONH

LuxR

(+)

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

O OO

ONH

(a) LuxI/R quorum sensing.(b) Peptide-mediated quorum sensing in development of competence.(c) Quorum sensing in V. harveyi.

Quorum Sensing Systems

ComD: histidine kinase

the periplasmic LuxP protein

that is involved in recogni-tion of AI-2 is not

shown for simplicity.

Similar Signalling SystemsN-acyl-L-Homoserine Lactone Autoinducers in Bacteria

Species Relation to Host Regulate Production of I Gene R Gene

Vibrio fischeri marine symbiont Bioluminescence luxI luxR

Vibrio harveyi marine symbiont Bioluminescence luxL,M luxN,P,Q

Pseudomonas aeruginosa Human pathogen Virulence factors lasI lasR

Rhamnolipids rhlI rhlR

Yersinia enterocolitica Human pathogen ? yenI yenR

Chromobacterium violaceum Human pathogenViolaceum production Hemolysin Exoprotease

cviI cviR

Enterobacter agglomerans Human pathogen ? eagI ?

Agrobacterium tumefaciens Plant pathogen Ti plasmid conjugation traI traR

Erwinia caratovora Plant pathogenVirulence factors Carbapenem production

expI expR

Erwinia stewartii Plant pathogen Extracellular Capsule esaI esaR

Rhizobium leguminosarum Plant symbiont Rhizome interactions rhiI rhiR

Pseudomonas aureofaciens Plant beneficial Phenazine production phzI phzR

Circuits for Controlled Sender & Receiver

pLuxI-Tet-8 pRCV-3

Fragment of pRCV-32038 bp (molecule 4149 bp)

GFP(LVA)

LuxR lux P(L)

lux P(R)

rrnB T1 rrnB T1

• Genetic networks:

• Logic circuits:

VAI VAI

Fragment of pLuxI-Tet-81052 bp (molecule 2801 bp)

LuxIP(LtetO-1) T1

aTc

luxI VAI

* E. coli strain expresses TetR (not shown)

*

VAI

LuxRGFP

tetR

aTc

00

receiverssenders

overlay

receivers senders

overlay

Remarks About Control Theory

Becskei & Serrano, Nature 2000

Model of tryptophan biosynthesis

0 5 10 15 200.8

0.85

0.9

0.95

1

1.05

Time (minutes)

[P]

h = 3

h = 0

0 2 4 6 8 10-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Frequency

Lo

g(S

n/S

0)

h = 3

h = 0

Spectrum

Time response

Robust

Yet fragile

0 2 4 6 8 10-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Frequency

Lo

g(S

n/S

0)

h = 3

h = 2

h = 1

h = 0

log )nxF(

Tighter steady-stateregulation

Transients, Oscillations

log )nx d constant F(

Theorem

log )nx d constant F(

log|S |

Tighter regulation

Transients, Oscillations

Biological complexity is dominated by the evolution of

mechanisms to more finely tune this robustness/fragility tradeoff.

This tradeoff is a law.

log )nx d constant F(

log|S |

Define log "fragility" ( )nS S x F

Conservation of “fragility”

Bacterial chemotaxis

Yi et al. PNAS 2000

Integral control

Integral Control