design of digital logic by genetic regulatory networks ron weiss department of electrical...

Design of Digital Logic by Genetic Regulatory Networks

Ron Weiss

Department of Electrical Engineering

Princeton University

Computing Beyond Silicon Summer School, Caltech, Summer 2002

E. coli

Diffusing signal

Programming Cell Communities

proteins

Program cells to perform various tasks using:• Intra-cellular circuits

– Digital & analog components• Inter-cellular communication

– Control outgoing signals, process incoming signals

Programmed Cell Applications

analytesource

Analyte source detection

Reporterrings

Pattern formation

• Biomedical– combinatorial gene regulation with few inputs; tissue engineering

• Environmental sensing and effecting– recognize and respond to complex environmental conditions

• Engineered crops– toggle switches control expression of growth hormones, pesticides

• Cellular-scale fabrication– cellular robots that manufacture complex scaffolds

Outline

• In-vivo logic circuits

• Intercellular communications

• Signal processing and analog circuits

• Programming cell aggregates

A Genetic Circuit Building Block

Digital Inverter Threshold Detector

Amplifier Delay

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

Why Digital?

• We know how to program with it– Signal restoration + modularity = robust complex

circuits

• Cells do it– Phage λ cI repressor: Lysis or Lysogeny?

[Ptashne, A Genetic Switch, 1992]– Circuit simulation of phage λ

[McAdams & Shapiro, Science, 1995]

• Also working on combining analog &digital circuitry

BioCircuit Computer-Aided Design

SPICE BioSPICE

steady state dynamics intercellular

• BioSPICE: a prototype biocircuit CAD tool– simulates protein and chemical concentrations– intracellular circuits, intercellular communication– single cells, small cell aggregates

Genetic Circuit Elements

inputmRNA ribosome

promoter

outputmRNA ribosome

operator

translation

transcription

RNAp

RBS RBS

Modeling a Biochemical Inverter

input

output

repressor

promoter

A BioSPICE Inverter Simulation

input

output

repressor

promoter

“Proof of Concept” Circuits• Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]

• They work in vivo – Flip-flop [Gardner & Collins, 2000]– Ring oscillator [Elowitz & Leibler, 2000]

• However, cells are very complex environments– Current modeling techniques poorly predict behavior

time (x100 sec)

[A]

[C]

[B]

B_S

_R

A

_[R]

[B]

_[S]

[A]

time (x100 sec)

time (x100 sec)

RS-Latch (“flip-flop”) Ring oscillator

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

The Toggle Switch[Gardner & Collins, 2000]

pIKE = lac/tetpTAK = lac/cIts

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

promoter

protein coding sequence

The Ring Oscillator[Elowitz, Leibler 2000]

Example of Oscillation

Evaluation of the Ring Oscillator

Reliable long-term oscillation doesn’t work yet: Will matching gates help? Need to better understand noise Need better models for circuit design

[Elowitz & Leibler, 2000]

A Ring Oscillator with Mismatched Inverters

A = original cI/λP(R)

B = repressor binding 3X weaker

C = transcription 2X stronger

Device Physics in Steady State

Transfer curve: gain (flat,steep,flat) adequate noise margins

[input]

“gain”

0 1

[output]

• Curve can be achieved with certain dna-binding proteins• Inverters with these properties can be used to build complex circuits

“Ideal” 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)

Drive Input Levels by Varying Inducer

0

100

1000

IPTG

YFP

lacI[high]

0(Off) P(lac)

P(lacIq)

lacIP(lacIq)

YFPP(lac)

IPTG

IPTG (uM)

promoter

protein coding sequence

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 CFP/YFP calibration

Controlling Input Levels

Cell Population Behavior

Red = pPROLARRest = pINV-102 with IPTG (0.1 to 1000 uM)

CFP: a Weak Fluorescent Protein

Induction of CFP expression

IPTG

Fluorescence

Measuring a Transfer Curve for lacI/p(lac)

aTc

YFPlacICFP

tetR[high]0

(Off) P(LtetO-1)

P(R)

P(lac)

measure TC

tetRP(R)

P(Ltet-O1)

aTcYFPP(lac)

lacI CFP

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:

high gainhigh gain

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

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]

The Cellular Gate LibraryAdd the cI/P(R) Inverter

OR1OR2 structural gene

P(R-O12)

• cI is a highly efficient repressorcooperative

binding

IPTG

YFPcI

CFPlacI[high]0

(Off) P(R)P(lac)

• Use lacI/p(lac) as driver

highgain

cI bound to DNA

Initial Transfer Curve for cI/P(R)

lacIP(lacIq)

P(lac)

IPTGYFPP(R)

cI CFP

Recall Inverter Components

inputmRNA ribosome

promoter

outputmRNA ribosome

operator

translation

transcription

RNAp

RBS RBS

Functional Composition of an Inverter

“clean” signal digital inversionscale input invert signal

translation

0 10 1

0 1

+ + =

“gain”

0 1

+ + =

cooperativebinding

transcription inversion

input mRNA

input protein

bound operators

output mRNA

Genetic Process Engineering I:Reducing Ribosome Binding Site Efficiency

RBS

translation

start

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

translationstage

Inversion

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



Experimental Results forcI/P(R) Inverter with Modified RBS

Genetic Process Engineering II:Mutating the P(R) operator

BioSPICE Simulation

orig: TACCTCTGGCGGTGATAmut4: TACATCTGGCGGTGATAmut5: TACATATGGCGGTGATAmut6 TACAGATGGCGGTGATA

OR1

cooperativebinding

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



Genetic Process Engineering

• Genetic modifications required to make circuit work

• Need to understand “device physics” of gates– enables construction of complex circuits

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

)

modifyRBS

mutateoperator

RBS

#1: modify RBS

#2: mutate operator

Self-perfecting Genetic Circuits[Arnold, Yokobayashi, Weiss]

optical micrograph of the μFACS device

• Use directed evolution to optimize circuits• Screening criteria based on transfer curve• Initial results are promising

Lab-on-a-chip: μFACS [Quake]

Molecular Evolution of the Circuit

Prediction of Circuit Behavior

1

10

100

1,000

0.1 1.0 10.0 100.0

1

10

100

1,000

0.1 1.0 10.0 100.0

=?

Outputsignal

Inputsignal

1

10

100

1,000

0.1 1.0 10.0 100.0

Can the behavior of a complex circuit be predicted using only

the behavior of its parts?

Prediction of Circuit Behaviorpreliminary results

YFP

aTc#

cells

tetRP(bla)

P(tet)

aTccIP(lac)

lacI CFP YFPP(R)

design of digital logic by genetic regulatory networks ron weiss department of electrical...

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