Programming Cells Environment actuators sensors Biochemical logic - - PDF document

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The Device Physics of Cellular Logic Gates

Ron Weiss

Department of Electrical Engineering Princeton University

NSC-1

Programming Cells

0.5µm

Biochemical logic circuit

Environment

sensors actuators

• Understand and engineer:

– Genetic regulatory networks – Cell-cell communications

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Programmed Cell Applications

• “Real time” cellular debugger

– detect conditions that satisfy logic statements – maintain history of cellular events

• Environmental

– sense & respond to complex environmental conditions

• Biomedical

– combinatorial gene regulation with few inputs

• Molecular-scale fabrication

– cellular robots that manufacture complex scaffolds

Programming Cells

proteins

synthesize & insert plasmid (plasmid = “user program”)

Biochemical logic circuit

0.5µm

3

A Biochemical Inverter

signal = concentration of specific molecules (mRNA) computation = regulated mRNA and protein synthesis + decay

Outline

• In-vivo digital circuits
• Cellular gates: Inverter, Implies
• BioSPICE circuit simulations & design
• Measuring & modifying “device physics”
• Cell-cell Signaling

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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

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 R1 X Y Z

=

gene gene gene

NAND NOT

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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

“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]

• Models poorly predict their 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

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Actual Behavior of Ring Oscillator

[Elowitz & Leibler, 2000]

The Cellular Gate Library

• Assembled and characterized a library of gates

– Constructed and measured gates using 4 genetic elements

• lac, tet, cI, lux
• Genetic process engineering

– Different elements have widely varying characteristics

• Modify “device physics” of gates until they match

– Created 16 variations of cI in order to match with lac:

• modified repressor/operator affinity
• modified RBS efficiency
• Established component evaluation criteria

– Initially, focused on steady state behavior

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Device Physics in Steady State

Transfer curve:

gain (flat,steep,flat) adequate noise margins

[input]

“gain”

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

• utput gene

R

YFP CFP

inverter

• Also, need to normalize CFP vs YFP

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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

• perator

promoter gene RNAP active repressor

• perator

promoter gene RNAP inactive repressor inducer no transcription transcription Repressor Inducer Output 1 1 1 1 1 1 1

Repressor Inducer Output

Drive Input Levels by Varying Inducer

100 1000

IPTG YFP lacI

[high] (Off)

P(lac)

P(lacIq)

lacI

P(lacIq)

YFP

P(lac)

IPTG

IPTG (uM)

promoter protein coding sequence

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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) FL1

pINV-112-R1 pINV-102

Also use for CFP/YFP calibration

Controlling Input Levels Measuring a Transfer Curve

for lacI/p(lac)

aTc YFP lacI CFP tetR

[high]

(Off)

P(LtetO-1) λP(R) P(lac)

measure TC

tetR

λP(R)

P(Ltet-O1)

aTc

YFP

P(lac)

lacI CFP

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Transfer Curve Data Points

01 10

1 ng/ml aTc

200 400 600 800 1,000 1,200 1,400 1 10 100 1,000 10,000 Fluorescence (FL1) Events

undefined 10 ng/ml aTc 100 ng/ml aTc

200 400 600 800 1,000 1,200 1,400 1 10 100 1,000 10,000 Fluorescence (FL1) Events 200 400 600 800 1,000 1,200 1,400 1 10 100 1,000 10,000 Fluorescence (FL1) Events

1 10 100 1000 1 10 100 1000 Input (Normalized CFP) Output (YFP)

lacI/p(lac) Transfer Curve

aTc YFP lacI CFP tetR

[high]

(Off)

P(LtetO-1) λP(R) P(lac)

gain = 4.72 gain = 4.72

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Evaluating the Transfer Curve

• Noise margins:

200 400 600 800 1,000 1,200 1,400 1 10 100 1,000 Fluorescence Events

30 ng/ml aTc 3 ng/ml aTc

1 10 100 1,000 0.1 1.0 10.0 100.0 aTc (ng/ml) Fluorescence

• Gain / Signal restoration:

high gain high gain

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

The Cellular Gate Library

Add the cI/λP(R) Inverter

OR1 OR2

structural gene

λP(R-O12)

• cI is a highly efficient repressor

cooperative binding

IPTG YFP cI CFP lacI

[high]

(Off)

λP(R) P(lac)

• Use lacI/p(lac) as driver

high gain

cI bound to DNA

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Initial Transfer Curve for cI/λP(R)

1.00 10.00 100.00 1,000.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) Output (YFP)

lacI

P(lacIq)

P(lac)

IPTG

YFP λP(R) cI CFP

Inverter Components

input mRNA

ribosome

promoter

• utput

mRNA

ribosome

• perator

translation transcription

RNAp RBS RBS

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Functional Composition of an Inverter

“clean” signal digital inversion scale input invert signal ψΑ φΑ

translation

φΑ ρΑ

1 1

ρΑ ψΖ

1

+ + =

ψΖ ψΑ

“gain” 1

+ + =

cooperative binding transcription inversion

ψΑ = input mRNA φΑ = input protein ρΑ = bound operators ψΖ = output mRNA

Genetic Process Engineering I:

Reducing Ribosome Binding Site Efficiency

R B S

translation start Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak

ψΑ φΑ

translation stage

ψΖ ψΑ

Inversion

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1.00 10.00 100.00 1,000.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) Output (YFP)

pINV-107/pINV-112-R1 pINV-107/pINV-112-R2 pINV-107/pINV-112-R3

Experimental Results for cI/λP(R) Inverter with Modified RBS

Genetic Process Engineering II:

Mutating the λP(R) operator

BioSPICE Simulation

• rig:

TACCTCTGGCGGTGATA mut4: TACA A A ATCTGGCGGTGATA mut5: TACA A A ATA A A ATGGCGGTGATA mut6 TACAGA AGA AGA AGATGGCGGTGATA

OR1

φΑ ρΑ

cooperative binding

ψΖ ψΑ

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Experimental Results for Mutating λP(R)

1.00 10.00 100.00 1,000.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) Output (YFP)

pINV- 107- mut4/pINV- 112- R3 pINV- 107- mut5/pINV- 112- R3 pINV- 107- mut6/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) Output (YFP)

modify RBS mutate

• perator

RBS #1: modify RBS #2: mutate operator

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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

=

?

Output signal Input signal

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?

Cell-cell Signaling

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Intercellular Communications

• Certain inducers useful for communications:
• 1. A cell produces inducer
• 2. Inducer diffuses outside the cell
• 3. Inducer enters another cell
• 4. Inducer interacts with repressor/activator change signal

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

main metabolism

The Intercellular AND Gate

• Inducers can activate activators:

– VAI (3-N-oxohexanoyl-L-Homoserine lacton) luxR

• Use as a logical AND gate:
• perator

promoter gene RNAP inactive activator

• perator

promoter gene RNAP active activator inducer no transcription transcription Output Activator Inducer Output 1 1 1 1 1 Activator Inducer

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Light organ

Eupryma scolopes 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) hv Luciferase Luciferase

P P Regulatory Genes Structural Genes

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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 LuxR LuxI P P

Low Cell Density Low Cell Density

luxR luxI luxC luxD luxA luxB luxE luxG LuxR LuxI (Light) hv (Light) hv

Luciferase Luciferase

P P

High Cell Density High Cell Density

LuxR

O O O O N H O O O O N H O O O O N H O O O O N H

LuxR (+)

O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H O O O O N H

Receiver cells

Circuits for Controlled Sender & Receiver

pLuxI-Tet-8 pRCV-3 VAI VAI

aTc

luxI VAI

VAI LuxR GFP

tetR aTc Receiver cells Sender cells Sender cells

tetR

P(tet)

luxI

P(Ltet-O1)

aTc

GFP(LVA)

Lux P(R)

luxR

Lux P(L)

+

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Time-Series Response to Signal

Fluorescence response of receiver (pRCV-3)

500 1000 1500 2000 2500 0:00 0:30 1:00 1:30 2:00 Time (hrs) Fluorescence pRCV-3 + pUC19 pRCV3 + pSND-1 pRCV-3 pRCV-3 + pRW-LPR-2 pRCV-3 + pTK-1 AI

p

• s

i t i v e c

• n

t r

• l

1 X V A I e x t r a c t direct signalling negative controls

Characterizing the Receiver

Response of receiver to different levels of VAI extract

200 400 600 800 1,000 1,200 0.1 1 10 Autoinducer Level Maximum Fluorescence

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25,000 50,000 75,000 10x AI Null 2 20 200 2,000 20,000 200,000 aTc (ng / ml) Receiver Fluorescence LuxTet4B9 RCV Only

Controlling the Sender’s Signal Strength

Dose response of receiver cells to aTc induction of senders receivers senders

• verlay

0.1mm

22 receivers senders

• verlay

20 µm

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Summary

• Built and characterized an initial cellular gate

library

• Genetic process engineering

– mutated logic elements to have desired behavior

• Using parts that match, built and tested several

small in-vivo digital circuits

– Reliable circuits with predictable behavior from reliable components with known behavior

• BioSPICE for circuit design/verification
• Cell-cell signaling to control gene expression

Acknowledgments

• Tom Knight
• Gerald J. Sussman
• Hal Abelson
• Nick Papadakis
• George Homsy
• Radhika Nagpal
• Dylan Hirsch-Shell
• Matt Frank
• Jered Floyd
• Jonathan Babb
• Glenn Paradis
• Subhayu Basu