Cellular Computation and Communications using Engineered Genetic Regulatory Networks
description
Transcript of Cellular Computation and Communications using Engineered Genetic Regulatory Networks
Cellular Computation and Communicationsusing Engineered Genetic Regulatory Networks
Ron WeissAdvisors: Thomas F. Knight, Gerald Jay Sussman, Harold Abelson
Artificial Intelligence Laboratory, MIT
Cellular Robotics
=C
CA
B
Dgene
gene
gene
AB
CD
NAND NOT
Biochemical Logic circuit
Environment
sensors actuators
Vision
• A new substrate for engineering: living cells– interface to the chemical world– cell as a factory / robot
• Logic circuit = process description– extend/modify behavior of cells
• Challenge: engineer complex, predictable behavior
Applications
• “Real time” cellular debugger– detect conditions that satisfy logic statements
– maintain history of cellular events
• Engineered crops / farm animals– toggle switches control expression of growth hormones, pesticides
• Biomedical– combinatorial gene regulation with few inputs
– sense & recognize complex environmental conditions
• Molecular-scale fabrication– cellular robots that manufacture complex scaffolds
“Programming” Cells
plasmid = “user program”
Biochemical Inverters
signal = concentration of specific proteinscomputation = regulated protein synthesis + decay
Engineering Challenges
• Map logic circuits to biochemical reactions• Circuit design and implementation:
– conventional interfaces
– sensitivities to chemical concentrations
– understand affinities of molecules to each other
– process engineering to adjust trigger levels, gains
– CAD tools (BioSpice)
Contributions
• Experimental results:– Built and characterized a small library of logic gates
• 4 different dna-binding proteins (lacI, tetR, cI, luxR)
• 12 modifications of gates based on cI protein
• transfer functions (input/output relationship)
– Built and tested several logic circuits • combined 3 gates based on transfer functions
– Engineered communication between cells• chemical diffusions carry message
• CAD tools and program design:– BioSpice (circuit design/verification)
– Microbial Colony Language
Outline
• A Model for Programming Biological Substrates– Example: Pattern formation
– Microbial Colony language
• In-vivo digital circuits– Cellular gates: Inverter, Implies
– BioSpice circuit simulations & design
– Measuring and modifying “device physics”
• Intercellular communications
– Additional gate: AND
– BioSpice simulations & design
– Measuring “device physics”
Programming Biological Substrates
• Constraints/Characteristics:– Simple, unreliable elements– Local, unreliable communication– Elements engineered to perform tasks
• Example task: form cellular-scale patterns
Another Example: Differentiation
Cells differentiate into bands of alternating C and D type segments.
A program for creating segments:
(start Crest ((send (make-seg C 1) 3)))
((make-seg seg-type seg-index) (and Tube (not C) (not D)) ((set seg-type) (set seg-index) (send created 3)))
(((make-seg) (= 0)) Tube ((set Bottom)))
(((make-seg) (> 0)) Tube ((unset Bottom)))
(created (or C D) ((set Waiting 10)))
(* (and Bottom C 1 (Waiting (= 0))) ((send (make-seg D 1) 3)))
(* (and Bottom D 1 (Waiting (= 0))) ((send (make-seg C 2) 3)))
(* (and Bottom C 2 (Waiting (= 0))) ((send (make-seg D 2) 3)))
(* (and Bottom D 2 (Waiting (= 0))) ((send (make-seg C 3) 3)))
Microbial Colony Language (MCL)
message condition actions
How can we accomplish this?
• Boolean state variables– DNA binding proteins
• Biochemical logic circuits– genetic regulatory networks
• Intercellular signaling chemicals– enzymes that make small molecules
biocompiler: MCL genetic circuits
Outline
• Programming Biological Substrates– Pattern Formation
– Microbial Colony language
• In-vivo digital circuits– Cellular gates: Inverter, Implies
– BioSpice circuit simulations & design
– Measuring and modifying “device physics”
• Intercellular communications
– Additional gate: AND
– BioSpice simulations & design
– Measuring “device physics”
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]
• Ultimately, combine 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
R1X
Y
Z= gene
gene
gene
NAND NOT
Examples of Useful Circuits
• Logic statements:
– (x AND y AND z) OR (NOT u)
• Decoders:
– Turn ON 1 of 8 genes using only 3 inputs
• Counters
• Memory, Toggle switches
• Clocks
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
“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
Evaluation of the Ring Oscillator
Reliable long-term oscillation doesn’t work yet Need to match gates
[Elowitz & Leibler, 2000]
Measuring & Modifying “Device Physics”
• Why?– Different elements have widely varying characteristics
– Need to be matched
• Assembled and characterized a library of components – Constructed and measured gates using 4 genetic candidates
• lac, tet, cI, lux
– Created 12 variations of cI in order to match with lac:• modified repressor/operator affinity
• modified RBS efficiency
• other mechanisms: protein decay, promoter strength, etc..
• Established component evaluation criteria – Initially, focused on steady state behavior
Steady-State Behavior: Inverter
“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!
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
Repressors & Inducers
• 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
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:
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]
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
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
Process Engineering II:Mutating the P(R)
BioSpice Simulations
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
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
)
Outline
• Programming Biological Substrates– Pattern Formation
– Microbial Colony language
• In-vivo digital circuits– Cellular gates: Inverter, Implies
– BioSpice circuit simulations & design
– Measuring and modifying “device physics”
• Intercellular communications
– Additional gate: AND
– BioSpice simulations & design
– Measuring “device physics”
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)
mainmetabolism
Activators & Inducers
• Inducers can activate activators:– VAI (3-N-oxohexanoyl-L-Homoserine lacton) luxR
• Use as a logical AND gate:
operatorpromoter gene
RNAP
inactiveactivator
operatorpromoter gene
RNAP
activeactivator
inducerno transcription transcription
Output
Activator Inducer Output
0 0 00 1 01 0 01 1 1
Activator
Inducer
BioSpice: Intercellular Communications
chemicalconcentration
• Small simulation: – 4x4 grid
– 2 cells (outlined)
(1) original I = 0
(2) introduce D send msg M
(3) recv msg set I
(4) msg decays I latched
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)
hvLuciferaseLuciferase
P
P
Regulatory Genes Structural Genes
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
Similar Signalling Systems
N-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
Experimental Setup
• BIO-TEK FL600 Microplate Fluorescence Reader
• Costar Transwell microplates and cell culture inserts with permeable membrane (0.1μm pores)
• Cells separated by function:– Sender cells in the bottom well
– Receiver cells in the top well
insert
Time-Series Response to Signal
Fluorescence response of receiver (pRCV-3)
0
500
1000
1500
2000
2500
0:00 0:30 1:00 1:30 2:00
Time (hrs)
Flu
ore
sce
nce
pRCV-3 + pUC19
pRCV3 + pSND-1
pRCV-3
pRCV-3 + pRW-LPR-2
pRCV-3 + pTK-1 AI
positive control
10X VAI extra
ct
direct signalling
negative controls
Characterizing the Receiver
Response of receiver to different levels of VAI extract
0
200
400
600
800
1,000
1,200
0.1 1 10
Autoinducer Level
Max
imu
m F
luo
resc
ence
0
25,000
50,000
75,000
aTc (ng / ml)
Rec
eive
rF
luo
resc
ence
LuxTet4B9RCV Only
Controlling the Sender’s Signal Strength
Dose response of receiver cells to aTc induction of senders
receiverssenders
overlay
receivers senders
overlay
Summary
• Built, characterized, and modified a library of cellular gates (“TTL Data Book”)
• Using parts that match, built and tested several small in-vivo digital circuits
• Engineered and tested programmable intercellular communications
• BioSpice (circuit design/verification)
• Microbial Colony Language
Future Work• New programming paradigms • Bio-compiler • Additional CAD tools • Bio-fab
– Large scale circuit design, production, and testing
• Simpler & more complex organisms:– Eukaryotes
– Mycoplasmas
• Biologically inspired logic gates• Engineer multicellular organisms• Molecular scale fabrication
vs.