automated)design)of)synthe3c) biology)feedbackcircuits) · • dead code elimination •...

Automated  Design  of  Synthe3c  Biology  Feedback  Circuits  

Jacob  Beal,  Aaron  Adler  

IBE  Conference  March,  2012  

Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors and not DARPA or the U.S. Government. 1  

Vision: WYSIWYG Synthetic Biology

Bioengineering should be like document preparation:

2  

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 ?  

3  

[Purnick  &  Weiss,  ‘09]  

Why a tool-chain?

Organism  Level  Descrip3on  

Cells    

This  gap  is  too  big  to  cross  with  a  single  method!  

4  

The TASBE architecture:

Organism  Level  Descrip3on  

Abstract  Gene3c  Regulatory  Network  

DNA  Parts  Sequence  

Assembly  Instruc3ons  

Cells    

High level simulator

Coarse chemical simulator

Testing

High  Level  Descrip3on  If detect explosives: emit signal If signal > threshold: glow red

Detailed chemical simulator

Modular  architecture  also  open  for  flexible  choice  of  organisms,  protocols,  methods,  …  

5  

Ron  Weiss  

Douglas  Densmore  

Collaborators:  

A Tool-Chain Example

(yellow (not (cyan (Dox))))!

Dox   cyan   not   yellow  

rtTA   CFP   B   EYFP  

Dox  

pHef1a   rtTA   pTre   CFP   pTre   LacI   pHef1a-­‐LacO1Oid  

EYFP  mirff4   4xff4  

6  

Today’s focus: BioCompiler

Organism  Level  Descrip3on  

Abstract  Gene3c  Regulatory  Network  

DNA  Parts  Sequence  

Assembly  Instruc3ons  

Cells    

High level simulator

Coarse chemical simulator

Testing

High  Level  Descrip3on  If detect explosives: emit signal If signal > threshold: glow red

Detailed chemical simulator

Compila3on  &  Op3miza3on  

7  

Transcriptional Logic

8  

Motif-Based Compilation

•  High-level primitives map to GRN design motifs –  e.g. logical operators:

(primitive not (boolean) boolean :grn-motif ((P high R- arg0 value T)))

arg0   value  

9  

  High-level primitives map to GRN design motifs   e.g. logical operators, actuators:

(primitive green (boolean) boolean :side-effect :type-constraints ((= value arg0)) :grn-motif ((P R+ arg0 GFP|arg0 value T)))

GFP   value  arg0  

Motif-Based Compilation

10  

  High-level primitives map to GRN design motifs   e.g. logical operators, actuators, sensors:

(primitive IPTG () boolean :grn-motif ((P high LacI|boolean T) (RXN (IPTG|boolean) represses LacI) (P high R- LacI value T)))

value  LacI  

IPTG  

Motif-Based Compilation

11  

  Functional program gives dataflow computation:

(green (not (IPTG)))

IPTG   not   green  

Motif-Based Compilation

12  

  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

13  

LacI   A  

IPTG  

B   GFP  

LacI   A  

IPTG  

B   GFP  

Copy  Propaga<on  

LacI   A  

IPTG  

GFP  

Dead  Code  Elimina<on  

LacI   A  

IPTG  

GFP  

Dead  Code  Elimina<on  

Optimization

14  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Merge Duplicate Inputs •  NOR Compression

15  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

16  

A  

B  

A  

B  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

17  

A  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

18  

A  

B  

C  

C  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

19  

A  

B  

B  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

GRN-specific 20  

A  

B  

C  

A  

A  

C  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

GRN-specific 21  

B  C  

D  

A  

C   D  A  

GRN Optimization Library

•  Copy Propagation •  Dead Code Elimination •  Double-Negative Elimination •  Constant Eliminator •  CSE: Output Consolidation •  CSE: Input Merge •  NOR Compression

GRN-specific 22  

C   D  

A  

A  

D  

A  

A  

(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)))

Feedback System: SR-Latch

23  

IPTG   not   green  

aTc  not  or  

or  

(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)))

Feedback System: SR-Latch

LacI   B  

IPTG  

I  

G  

I  F  

GFP  

D  E1   E2  

A  

aTc  J  

H  C  

J  TetR  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors   24  

(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)))

Optimization of Complex Designs

LacI   B  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

G  

I  F  

GFP  

D  E1   E2  

A  

aTc  J  

H  C  

J  TetR  Copy  Propaga<on  25  

E1  

(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)))

Optimization of Complex Designs

LacI  

B  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

G  

I  F  

GFP  

D  

A  

aTc  J  

H  C  

J  

TetR  

Common  Subexp.  Elim.  26  

E1  

(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)))

Optimization of Complex Designs

LacI  

B  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

G  

I  F  

GFP  

D  

A  

aTc  H   J  C  

J  

TetR  

NOR  Compression  H  

27  

E1  

(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)))

Optimization of Complex Designs

LacI  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

G  

I  F  

GFP  

aTc  H  

TetR  

Dead  Code  Elimina<on  H  

28  

E1  

(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)))

Optimization of Complex Designs

LacI  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

G  

I  F  

GFP  

aTc  H  

TetR  

Copy  Propaga<on  H  

29  

H  E1  

(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)))

Optimization of Complex Designs

LacI  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  G  

I  F  

GFP  

aTc  

TetR  

Common  Subexp.  Elim.  H  

30  

H  

(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)))

Optimization of Complex Designs

LacI  

IPTG  

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I  

I  F  

GFP  

aTc  

TetR  

Dead  Code  Elimina<on  H  

31  

H  

(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)))

Optimization of Complex Designs

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

I   F   GFP  

H  

LacI  

IPTG  

TetR  aTc  

I  

32  

H  

(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)))

Optimization of Complex Designs

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

F   GFP  

H  

LacI  

IPTG  

TetR  aTc  

F  

NOR  Compression  

I  

I  

33  

H  

(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)))

Optimization of Complex Designs

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

F   GFP  

H  

LacI  

IPTG  

TetR  aTc  

F  

Dead  Code  Elimina<on  34  

GFP  

Optimization of Complex Designs

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)))

Unop<mized:  15  func<onal  units,  13  transcrip<on  factors  

H  

35  

Compilation & Optimization Results:

•  Automated GRN design for arbitrary boolean logic and feedback systems –  Verification in ODE simulation

•  Optimization competitive with human experts: –  Test systems have 25% to 71% complexity reduction –  Optimized systems are often homologous with hand

designed networks •  Routine use within team

36  

Onward Through the Tool-Chain…

(yellow (not (cyan (Dox))))!

Dox   cyan   not   yellow  

rtTA   CFP   B   EYFP  

Dox  

pHef1a   rtTA   pTre   CFP   pTre   LacI   pHef1a-­‐LacO1Oid  

EYFP  mirff4   4xff4  

37  

Contributions

•  Boolean & feedback logic circuits can be expressed in high-level language.

•  GRN-specific optimizations allow dramatic optimization of circuit size.

•  Automatically generated circuits are competitive with hand-designs created by experts.

Next steps: automatic verification, numerical computation, intercellular communication

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