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High-‐Level BioDesign Automa5on
Jacob Beal
SemiSynBio February, 2013
Overview
Is biology too hard for abstraction? High-Level BDA is possible now!
• Tool-chains for BDA • Compiling from HLL to biological circuits • Building computational device libraries
Vision: WYSIWYG Synthetic Biology
Bioengineering should be like document preparation:
3
Why is this important?
• Breaking the complexity barrier:
• Multiplication of research impact • Reduction of barriers to entry
*Sampling of systems in publica<ons with experimental circuits
207
2,100 2,700
7,500 14,600
32,000
583,000 1,080,000
100
1,000
10,000
100,000
1,000,000
1975 1980 1985 1990 1995 2000 2005 2010
Length in base pa
irs
Year
DNA synthesis Circuit size ?
4
[Purnick & Weiss, ‘09]
Why a tool-chain?
Organism Level Descrip5on
Cells
This gap is too big to cross with a single method!
5
The TASBE tool-chain architecture:
Organism Level Descrip5on
Abstract Gene5c Regulatory Network
DNA Parts Sequence
Assembly Instruc5ons
Cells
High level simulator
Coarse chemical simulator
Testing
High Level Descrip5on If detect explosives: emit signal If signal > threshold: glow red
Detailed chemical simulator
Modular architecture also open for flexible choice of organisms, protocols, methods, …
6
Ron Weiss
Douglas Densmore
Collaborators:
A Tool-Chain Example
(def simple-sensor-actuator ()! (let ((x (test-sensor)))! (debug x)! (debug-2 (not x))))!
If detect explosives: emit signal If signal > threshold: glow red
Mammalian Target E. coli Target
A Tool-Chain Example
If detect explosives: emit signal If signal > threshold: glow red
blue
[expr]
Dox
not
yellow
!"##$%&'(
!"##$%&'(
!"##$%&'(
!"##$%&'(
Mammalian Target E. coli Target
green
[expr]
Ara
not
red
!"##$%&'(
!"##$%&'(
!"##$%&'(
!"##$%&'(
A Tool-Chain Example
If detect explosives: emit signal If signal > threshold: glow red
!"#$
%&'($ ()$ *+,-$
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!"##$%&'() !"##$%&'()
!"##$%&'()
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Mammalian Target E. coli Target
!"#$
%&'$!"#($ !)$ *&'$
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!"##$%&'()
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!"##$%&'() !"##$%&'()
*+,-) *+,-) *+,-)
A Tool-Chain Example
If detect explosives: emit signal If signal > threshold: glow red
Mammalian Target E. coli Target
!"#$%
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)*+,% !-#$.%
Transcription Factors
Promoters
)*+,%&'(%
Small Molecules
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Transcription Factors
Promoters
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Small Molecules
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GRN AGRN
Feature Database Canonical AGRN
GRN
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AGRN
Transcription Factors
Promoters
%&'($%&'$
Small Molecules
#*$
+$ #,$
#-$
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Transcription Factors
Promoters
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Small Molecules
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Feature Database Canonical AGRN
A Tool-Chain Example
If detect explosives: emit signal If signal > threshold: glow red
Mammalian Target E. coli Target
pC araC
pBad TetR GFP
araC TetR GFP RFPpTetpBad
Ara
pC
pTet
RFP
A Tool-Chain Example
If detect explosives: emit signal If signal > threshold: glow red
Mammalian Target E. coli Target
Uninduced Uninduced
Induced Induced
Focus: BioCompiler
Organism Level Descrip5on
Abstract Gene5c Regulatory Network
DNA Parts Sequence
Assembly Instruc5ons
Cells
High level simulator
Coarse chemical simulator
Testing
High Level Descrip5on If detect explosives: emit signal If signal > threshold: glow red
Detailed chemical simulator
Compila5on & Op5miza5on
13
Other tools aiming at high-‐level design: Cello, Eugene, GEC, GenoCAD, etc.
Transcriptional Logic Computations
14
Operators translated to motifs:
IPTG not green
LacI
A IPTG
B
GFP outputs outputs outputs arg0 arg0
LacI A
IPTG
B GFP
Motif-Based Compilation
15
(def sr-latch (s r) (letfed+ ((o boolean (not (or r o-bar))) (o-bar boolean (not (or s o)))) o))
(green (sr-latch (aTc) (IPTG)))
Design Optimization
LacI B
IPTG
I
G
I F
GFP
D E1 E2
A
aTc J
H C
J TetR
UnopEmized: 15 funcEonal units, 13 transcripEon factors 16
GFP
Design Optimization
LacI F
IPTG
TetR
H
aTc
F
Final Op>mized: 5 func>onal units 4 transcrip>on factors
(def sr-latch (s r) (letfed+ ((o boolean (not (or r o-bar))) (o-bar boolean (not (or s o)))) o))
(green (sr-latch (aTc) (IPTG)))
UnopEmized: 15 funcEonal units, 13 transcripEon factors
H
17
Automated Synthesis of Complex Designs
Example: 4-bit adder Example: 4-bit counter
Alfa1
Uppermost
Mike1
AmCyan
Barber
Homework1
Bravo1
Lipstick
Mike1
Bribe
Nuisance Paw
Charlie1
Modesty
Grammar
Companionship
Woolen Hinder
Cultivation
Hinder
Delta1
Rubbish
Grammar
Dox
rtTA
EBFP2
EYFP
Echo1
Homecoming
Ripen
Feast
Nuisance Tidy
Foxtrot1
Liar
Ripen
GFP
Golf1
Feast
Tidy
Grammar
Yankee1
Handwriting
mKate
Hinder
EYFP
Homecoming
Barber
Homework1
Mend
Hotel1
Stocking
IND4
IND4I
Delta1
IND5
IND5I
Echo1
IND6
IND6I
Foxtrot1
IND7
IND7I
Old
IND8
IND8I
Hotel1
IPTG
LacI
India1
Companionship
Jealous
Barber Handwriting
Jealousy
Handwriting
Charlie1
Liar
Lipstick
India1
Mend
Paw
Mike1
Punctual
Modesty
Woolen
Nuisance
GFP
Paw
AmCyan
Punctual
EBFP2
RSL
RheoAct_Rec
Redden1
Jealous
Bravo1
Ripen
Redden1
Rubbish
Stocking
Tidy
Widower1
Uppermost
Victor1
Jealousy
Widower1
Bribe
Woolen
Victor1
Yankee1
Cultivation
mKate
Alfa1
rtTA IND8I IND7I IND6I IND5I IND4I LacI RheoAct_Rec
Golf1
AmCyan
Attentive
Sow Whichever
Avoidance
Grammar
Basin
Rotten Breadth
Bicycle
Obedient
Breadth
Bribe
Nuisance
Charlie1
Juliet Homemade
Cheat
Mat
Companionship
Deceive
Congratulate
Basin
Correction
Lengthen
Coward
Congratulate
Deceit1
WoolenCompanionship
Avoidance
Ink
Deceive
Descendant
Descendant
Disappearance
Woolen Spoon
Dox
rtTA
EBFP2
EYFP
Feast
Mend
Golf
Liar
Grammar
EBFP2 Parcel1
Homecoming
Jealous
Inclusive
Homemade
Golf Slippery
Homework
Whiskey1 mKate
Inclusive
Ink
Jealous
Homecoming Sour
Juliet
Ripen
Lengthen
Liar
Lima
AmCyan Charlie1
Madden
Coward
Mat
Bicycle
Mend
Multiplication
Multiplication
Homework
Nuisance
Attentive
Oar
Bribe
Obedient
EYFP Sadden1
Parcel1
Rotten MaddenCompanionshipDisappearance
Paw
Ripen
Rotten
Cheat
Sadden1
Sow Oar Madden Weed
Slippery
Lima
Sour
Sow
Feast
Spoon
Weed
Whichever
Whiskey1
Golf Juliet Oar Paw
Woolen
Correction
mKate
Deceit1
rtTA
OpEmized compiler already outperforms human designers
Barriers & Emerging Solutions:
• Barrier: Availability of High-Gain Devices – Emerging Solution: combinatorial device libraries
based on TALs, ZFs, miRNAs
• Barrier: Characterization of Devices – Emerging solution: TASBE characterization method
• Barrier: Predictability of Biological Circuits – Emerging solution: EQuIP prediction method
TASBE Method: Calibrated, Precise Characterization
TAL14 TAL21
pCAG
Dox
T2A rtTA3 VP16Gal4 pTRE
EBFP2 pTRE
R1 pUAS-‐Rep1
EYFP pCAG mkate pCAG
Characterization High Quality Predictions
103 104 105 106 107 108104
105
106
107
108
109
IFP MEFL
OFP
MEF
L
Non Normalized Cascade TAL21 TAL14 Interpolated Prediction transfer curve
103 104 105 106 107 108104
105
106
107
108
109
IFP MEFL
OFP
MEF
L
Non Normalized Cascade LmrA TAL14 Interpolated Prediction transfer curve
LmrA TAL14 TAL21 TAL14
pCAG
Dox
T2A rtTA3 VP16Gal4 pTRE
EBFP2 pTRE
R1 pUAS-‐Rep1 pUAS-‐Rep2
EYFP R2 pCAG mkate pCAG
High Quality Cascade Predictions
103 104 105 106 107 108104
105
106
107
108
109
IFP MEFL
OFP
MEF
L
Non Normalized Cascade TAL21 TAL14 Interpolated transfer curve
103 104 105 106 107 108104
105
106
107
108
109
IFP MEFL
OFP
MEF
L
Non Normalized Cascade LmrA TAL14 Interpolated transfer curve
LmrA TAL14 TAL21 TAL14 Distribu5on + dynamics models good predic5ons
pCAG
Dox
T2A rtTA3 VP16Gal4 pTRE
EBFP2 pTRE
R1 pUAS-‐Rep1 pUAS-‐Rep2
EYFP R2 pCAG mkate pCAG
Summary
High-Level BDA is possible now! • EDA tool-chain approach works for BDA • Optimized biological circuits can be generated
automatically from high-level specifications • Emerging solutions for key barriers: device
libraries, characterization, prediction
• Many opportunities for EDA tool adaptation: – Combinatorial device design – Flexible protocol automation – Device characterization – Circuit optimization, verification, safety, debugging
.. and going from cells to processors…
Inference resources focused propor<onally on areas of interest
ASH volumetric region management [Pruteanu, Dulman & Langendoen, ‘10]
Proto global-‐to-‐local compila<on & manifold computa<on model
Distor<on of computa<on around temporary and permanent faults
Spa>al Compu>ng Process Management
Characterization & Design Tools Online
https://synbiotools.bbn.com/
Acknowledgements:
Aaron Adler Joseph Loyall Rick Schantz Fusun Yaman
Ron Weiss Jonathan Babb Noah Davidsohn Ting Lu
Douglas Densmore Evan Appleton Swapnil Bhatia Traci Haddock Chenkai Liu Viktor Vasilev
26
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