Post on 12-Jan-2016
transcript
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
pINV-107/pINV-112-R2
pINV-107/pINV-112-R3
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
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)
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)