Automatic Compilation from High-Level Bio-Languages to ... › Talks › IWBDA-2010.pdf ·...
Transcript of Automatic Compilation from High-Level Bio-Languages to ... › Talks › IWBDA-2010.pdf ·...
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Automatic Compilation from High-Level Bio-Languages to Genetic Regulatory Networks
Jacob Beal, Ting Lu, Ron Weiss
IWBDA, June 2010
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Goal: High-Level Biological Design
(def band-detector (signal lo hi) (and (> signal lo) (< signal hi)))
(let ((v (diffuse (aTc) 0.8 0.05))) (green (band-detect v 0.2 1)))
High-LevelBio-focusedLanguage
OptimizeCompile
Genetic RegulatoryNetwork
Sim
ula te
Assem
ble
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Tool Chain Vision
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Tool Chain Vision
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Outline
● Compositional Design● Motif-Based Compilation● Simulation Results
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ribosome
Computation via Transcription Network
DNA
RNARNA polymerase
promoter
regulatoryprotein
ProteinDecay
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ribosome
Computation via Transcription Network
Stablizes at decay = production
DNA
RNA
promoter
regulatoryprotein
ProteinDecay
Signal = Concentration
RNA polymerase
Alternatives:
PoPS
RNA concentration
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X
Abstract GRN Design Space
Y
Z
XInputs:[none]
,,
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X
Abstract GRN Design Space
Y
Z
X
d [ X ]=Ri⋅1K Y−1[Y ]/DY
H Y
1[Y ]/DY H Y ⋅ 1K Z [Z ]/DZ
H Z
1[Z ]/DZ H Z −log2/ t X [ X ]
K ∈ [2,1000]
D ∈ [10,1000] nM
[X] ∈ [0,1000] nM
t > 300 s
H ∈ [1,4]
R < 10 nM/s
Inputs:[none]
,,
KY D
Y H
Y
KZ D
Z H
Z
tX
Ri
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Major Challenge: Interference
● Effective part characterics changed by:● Cellular context (endogenous pathways, synthetic parts)● Expression noise
● Our approach: noise-rejection
● Digital - static discipline: Vlow,out
< Vlow,in
< Vhigh,in
< Vhigh,out
But part variance makes a uniform standard impossible!
Vlow,in
Vhigh,in
Vlow,out
Vhigh,out
[In]
[Out]
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
![Page 17: Automatic Compilation from High-Level Bio-Languages to ... › Talks › IWBDA-2010.pdf · Effective part characterics changed by: Cellular context (endogenous pathways, synthetic](https://reader033.fdocuments.net/reader033/viewer/2022042411/5f290a856f332a740817aadb/html5/thumbnails/17.jpg)
Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
![Page 18: Automatic Compilation from High-Level Bio-Languages to ... › Talks › IWBDA-2010.pdf · Effective part characterics changed by: Cellular context (endogenous pathways, synthetic](https://reader033.fdocuments.net/reader033/viewer/2022042411/5f290a856f332a740817aadb/html5/thumbnails/18.jpg)
Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
![Page 19: Automatic Compilation from High-Level Bio-Languages to ... › Talks › IWBDA-2010.pdf · Effective part characterics changed by: Cellular context (endogenous pathways, synthetic](https://reader033.fdocuments.net/reader033/viewer/2022042411/5f290a856f332a740817aadb/html5/thumbnails/19.jpg)
Parameterized Standards Families
● Identify “standards family” parameter relation
● Create library of characterized part variants
● Adjust part choice to match on junctions
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Experimental Input to Family Relation
[Basu & Weiss, '05]
Model constrained by characterization experiments...
[Karig, '07][Subramanian, '08]
[Weiss, '01]
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Draft Simulation-Based Standards Family Generation
Need: K,H,D,R,t,max [X]
● Choose max [X]
● Assuming large K, amplification of 0.5K when [X]/D ≈ 1
● H → D, static discipline (higher H is better)
● Steady state max: [X]=production*t/log(2)
● production ≈ K*RExample:
max [X] = 500 nM H = 3R = 0.193 t = 1800 sK = 101 D = 190
Vlow,out
Vhigh,in
Vhigh,out
Vlow,in
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Draft Simulation-Based Standards Family Generation
Need: K,H,D,R,t,max [X]
● Choose max [X]
● Assuming large K, amplification of 0.5K when [X]/D ≈ 1
● H → D, static discipline (higher H is better)
● Steady state max: [X]=production*t/log(2)
● production ≈ K*RExample:
max [X] = 500 nM H = 3R = 0.193 t = 1000 sK = 101 D = 80
Vlow,out V
high,in
Vhigh,out
Vlow,in
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Draft Simulation-Based Standards Family Generation
Need: K,H,D,R,t,max [X]
● Choose max [X]
● Assuming large K, amplification of 0.5K when [X]/D ≈ 1
● H → D, static discipline (higher H is better)
● Steady state max: [X]=production*t/log(2)
● production ≈ K*RExample:
max [X] = 500 nM H = 3R = 0.193 t = 1000 sK = 101 D = 80
Driver for experimental part creation & characterization!
Vlow,out V
high,in
Vhigh,out
Vlow,in
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Outline
● Compositional Design● Motif-Based Compilation● Simulation Results
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Motif-Based Compilation
● High-level primitives map to GRN design motifs● e.g. logical operators:
(primitive not (boolean) boolean :bb-template ((P 0.193 R- arg0 RBS outputs T)))
arg0 output
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Motif-Based Compilation
● High-level primitives map to GRN design motifs● e.g. logical operators, actuators:
(primitive green (scalar) scalar :side-effect :bb-template ((P R+ arg0 RBS GFP outputs T)))
GFP outputarg0
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Motif-Based Compilation
● High-level primitives map to GRN design motifs● e.g. logical operators, actuators, sensors:
(primitive IPTG () scalar :bb-template ((P 0.193 RBS LacI T) (rxn LacI (IPTG 180000) -> LacI*)
(P 0.193 R- LacI RBS outputs T)))
outputLacI
IPTG
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Motif-Based Compilation
● Functional program gives dataflow computation:
● Operators translated to motifs:
● Standards family sets chemical constants● Optimizers simplify network
(green (not (IPTG)))
LacI A
IPTG
B GFP
IPTG not green
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Motif-Based Compilation
● Functional program gives dataflow computation:
(green (not (IPTG)))
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Motif-Based Compilation
● Functional program gives dataflow computation:
(green (not (IPTG)))
IPTG not green
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Motif-Based Compilation
● Operators translated to motifs:● Standards family sets chemical constants
IPTG not green
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Motif-Based Compilation
● Operators translated to motifs:● Standards family sets chemical constants
not greenLacI A
IPTG
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Motif-Based Compilation
● Operators translated to motifs:● Standards family sets chemical constants
greenLacI A
IPTG
B
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Motif-Based Compilation
● Operators translated to motifs:● Standards family sets chemical constants
LacI A
IPTG
B GFP C
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Motif-Based Compilation
● Optimizers simplify network
LacI A
IPTG
B GFP C
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Motif-Based Compilation
● Optimizers simplify network
LacI A
IPTG
B GFP
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Motif-Based Compilation
● Optimizers simplify network
LacI A
IPTG
GFP
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Outline
● Compositional Design● Motif-Based Compilation● Simulation Results
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Simulation Results
● Prototype compiler generates GRNs that simulate correctly for a limited language subset
● Example: 2-bit adder
a1a0
b1b0
x1x0
carry
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Simulation Results
● Prototype compiler generates GRNs that simulate correctly for a limited language subset
● Example: 2-bit adder
a1a0
b1b0
x1x0
carry
1
true
3
2
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Simulation Results
● Prototype compiler generates GRNs that simulate correctly for a limited language subset
● Example: 2-bit adder
a1a0
b1b0
x1x0
carry2
3
1
true
1
00
1
1
1
1
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Simulation Results
● Prototype compiler generates GRNs that simulate correctly for a limited language subset
● Example: 2-bit adder
a1a0
b1b0
x1x0
carryaTcIPTG
C4HSL
3OC12
HSL
blue
redgreen
2
3
1
true
1
00
1
1
1
1
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Simulation Results
● Prototype compiler generates GRNs that simulate correctly for a limited language subset
● Example: 2-bit adder(macro xor (a b) (muxor (muxand ,a (not ,b))
(muxand ,b (not ,a))))
(macro 2bit-adder (a1 a0 b1 b0) (all (green (xor ,a0 ,b0)) ; x_0 low bit (let ((c0 (muxand ,a0 ,b0))
(x1 (xor ,a1 ,b1))) (red (xor x1 c0)) ; x_1 high bit (blue (muxor (muxand x1 c0) ; carry bit (muxand ,a1 ,b1))))))
(2bit-adder (aTc) (IPTG) (C4HSL) (3OC12HSL))
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Simulation Results
● Compiled 2-bit adder (unoptimized)● 60 signal chemicals● 52 regulatory regions
● Generated ODE simulation in MATLAB
00+00
Bit 0Bit 1Carry
10+01 11+10 01+01
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On to optimization...
● Adapted classical techniques can be powerful:
Band detector optimization from [Beal & Bachrach, '08]
(def band-detector (signal lo hi) (and (> signal lo) (< signal hi))) (let ((v (diffuse (aTc) 0.8 0.05))) (green (band-detect v 0.2 1)))
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Tool Chain Vision
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Contributions
● Parameterized standards identify all chemical parameters that can produce digital logic in transcriptional networks.
● Prototype motif-based compiler automatically maps high-level programs into GRNs.
● Automatically generated MATLAB ODE simulations verify that GRNs implement program specification.