Presentation is loading. Please wait.

Presentation is loading. Please wait.

Proto BioCompiler Jacob Beal October, 2011 Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors.

Published byJake Bishop Modified over 9 years ago

Similar presentations

Presentation on theme: "Proto BioCompiler Jacob Beal October, 2011 Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors."— Presentation transcript:

1 Proto BioCompiler Jacob Beal October, 2011 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

2 Vision: WYSIWYG Synthetic Biology Bioengineering should be like document preparation: 2

3 Vision: WYSIWYG Synthetic Biology Bioengineering should be like document preparation: 3

4 Why is this important? Breaking the complexity barrier: Multiplication of research impact Reduction of barriers to entry *Sampling of systems in publications with experimental circuits DNA synthesis Circuit size ? 4

5 Why a tool-chain? Organism Level Description Cells This gap is too big to cross with a single method! 5

6 The TASBE architecture: Organism Level Description Abstract Genetic Regulatory Network DNA Parts Sequence Assembly Instructions Cells High level simulator Coarse chemical simulator Testing High Level Description 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

7 A Tool-Chain Example (yellow (not (cyan (Dox)))) 7

8 A Tool-Chain Example (yellow (not (cyan (Dox)))) Doxcyannotyellow 8

9 A Tool-Chain Example (yellow (not (cyan (Dox)))) Doxcyannotyellow rtTA CFP B B EYFP Dox 9

10 A Tool-Chain Example (yellow (not (cyan (Dox)))) Doxcyannotyellow rtTA CFP B B EYFP Dox pHef1artTA pTreCFP pTre LacI pHef1a- LacO1Oid EYFP mirff4 4xff4 10

11 A Tool-Chain Example (yellow (not (cyan (Dox)))) Doxcyannotyellow rtTA CFP B B EYFP Dox pHef1artTA pTreCFP pTre LacI pHef1a- LacO1Oid EYFP mirff4 4xff4 11

12 A Tool-Chain Example (yellow (not (cyan (Dox)))) Doxcyannotyellow rtTA CFP B B EYFP Dox pHef1artTA pTreCFP pTre LacI pHef1a- LacO1Oid EYFP mirff4 4xff4 12

13 Today’s focus: BioCompiler Organism Level Description Abstract Genetic Regulatory Network DNA Parts Sequence Assembly Instructions Cells High level simulator Coarse chemical simulator Testing High Level Description If detect explosives: emit signal If signal > threshold: glow red Detailed chemical simulator Compilation & Optimization 13

14 Transcriptional Logic 14

15 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 15

16 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 16

17 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 17

18 Functional program gives dataflow computation: (green (not (IPTG))) Motif-Based Compilation 18

19 Functional program gives dataflow computation: (green (not (IPTG))) IPTGnotgreen Motif-Based Compilation 19

20 Operators translated to motifs: IPTGnotgreen Motif-Based Compilation 20

21 Operators translated to motifs: IPTGnotgreen LacI A IPTG B GFP outputs arg0 Motif-Based Compilation 21

22 Operators translated to motifs: IPTGnotgreen LacIA IPTG B GFP LacI A IPTG B GFP outputs arg0 Motif-Based Compilation 22

23 LacIA IPTG B GFP Optimization 23

24 LacIA IPTG B GFP LacIA IPTG B GFP Copy Propagation Optimization 24

25 LacIA IPTG B GFP LacIA IPTG B GFP Copy Propagation LacIA IPTG GFP Dead Code Elimination Optimization 25

26 LacIA IPTG B GFP LacIA IPTG B GFP Copy Propagation LacIA IPTG GFP Dead Code Elimination LacIA IPTG GFP Dead Code Elimination Optimization 26

27 (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))) Complex System: Feedback Latch 27

28 (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))) Complex System: Feedback Latch LacI B IPTG IGIFGFPD E1 E2 A aTc JHC J TetR Unoptimized: 15 functional units, 13 transcription factors 28

29 (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 Unoptimized: 15 functional units, 13 transcription factors IGIFGFPD E1 E2 A aTc JHC J TetR Copy Propagation 29

30 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 Unoptimized: 15 functional units, 13 transcription factors I GIF GFP D A aTc J H C J TetR Common Subexp. Elim. 30

31 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 Unoptimized: 15 functional units, 13 transcription factors I GIF GFP D A aTc H J C J TetR NOR Compression H 31

32 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 Unoptimized: 15 functional units, 13 transcription factors I GIF GFP aTc H TetR Dead Code Elimination H 32

33 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 Unoptimized: 15 functional units, 13 transcription factors I GIF GFP aTc H TetR Copy Propagation H 33

34 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 Unoptimized: 15 functional units, 13 transcription factors IG I F GFP aTc TetR Common Subexp. Elim. H 34

35 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 Unoptimized: 15 functional units, 13 transcription factors I I F GFP aTc TetR Dead Code Elimination H 35

36 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 Unoptimized: 15 functional units, 13 transcription factors I F GFP H LacI IPTG TetR aTc I 36

37 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 Unoptimized: 15 functional units, 13 transcription factors F GFP H LacI IPTG TetR aTc F NOR Compression I I 37

38 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 Unoptimized: 15 functional units, 13 transcription factors F GFP H LacI IPTG TetR aTc F Dead Code Elimination 38

39 GFP Optimization of Complex Designs LacI F IPTG TetR H aTc F Final Optimized: 5 functional units 4 transcription 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))) Unoptimized: 15 functional units, 13 transcription factors H 39

40 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 40

41 pHef1artTA EYFP Hef1a-LacO1Oid mKateTrepLac Dox EBFPTre Realization with Characterized DNA parts pHef1a 41

42 Onward Through the Tool-Chain… (yellow (not (cyan (Dox)))) Doxcyannotyellow rtTA CFP B B EYFP Dox pHef1artTA pTreCFP pTre LacI pHef1a- LacO1Oid EYFP mirff4 4xff4 42

43 Proto is available http://proto.bbn.com/ (or google “MIT Proto”) Includes libraries, compiler, kernel, simulator, platforms Licensed under GPL (w. libc-type exception) BioCompiler is available on request 43

44 TASBE Project Team: Jacob Beal (PI) Aaron Adler Rick Schantz Fusun Yaman Joseph Loyall (PMs) Ron Weiss (co-PI) Jonathan Babb Noah Davidsohn Douglas Densmore (co-PI) Swapnil Bhatia Traci Haddock Viktor Vasilev Chenkai Liu Sponsored by: 44

Download ppt "Proto BioCompiler Jacob Beal October, 2011 Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors."

Similar presentations

TOPIC : SYNTHESIS DESIGN FLOW Module 4.3 Verilog Synthesis.

TOPIC : SYNTHESIS DESIGN FLOW Module 4.3 Verilog Synthesis.
ECE 667 - Synthesis & Verification - Lecture 2 1 ECE 667 Spring 2011 ECE 667 Spring 2011 Synthesis and Verification of Digital Circuits High-Level (Architectural)

ECE Synthesis & Verification - Lecture 2 1 ECE 667 Spring 2011 ECE 667 Spring 2011 Synthesis and Verification of Digital Circuits High-Level (Architectural)
Accurate Predictions of Genetic Circuit Behavior from Part Characterization and Modular Composition Jacob Beal, Noah Davidsohn, Aaron Adler, Fusun Yaman,

Accurate Predictions of Genetic Circuit Behavior from Part Characterization and Modular Composition Jacob Beal, Noah Davidsohn, Aaron Adler, Fusun Yaman,
TASBE Synthetic Biology Tools: Calibrated Flow Cytometry

TASBE Synthetic Biology Tools: Calibrated Flow Cytometry
IGEM 2007 ETH Zurich 04.06.2007. ETH Zurich iGEM Team 2 ETH Zurich team.

IGEM 2007 ETH Zurich ETH Zurich iGEM Team 2 ETH Zurich team.
High-Level BioDesign Automation Jacob Beal SemiSynBio February, 2013.

High-Level BioDesign Automation Jacob Beal SemiSynBio February, 2013.
Davidson College Synth-Aces Tamar Odle (’08), Oscar Hernandez (’06), Kristen DeCelle (’06), Andrew Drysdale (’07), Matt Gemberling (’06), and Nick Cain.

Davidson College Synth-Aces Tamar Odle (’08), Oscar Hernandez (’06), Kristen DeCelle (’06), Andrew Drysdale (’07), Matt Gemberling (’06), and Nick Cain.
Repressilator Presentation contents: The idea Experimental overview. The first attemp. The mathematical model. Determination of the appropiate parameters.

Repressilator Presentation contents: The idea Experimental overview. The first attemp. The mathematical model. Determination of the appropiate parameters.
From Spatial Computing to Tactical Command of Swarms Jacob Beal 2012 AAAI Fall Symposium: Human Control of Bio-Inspired Swarms November, 2012 Work partially.

From Spatial Computing to Tactical Command of Swarms Jacob Beal 2012 AAAI Fall Symposium: Human Control of Bio-Inspired Swarms November, 2012 Work partially.
Combinatorial Synthesis of Genetic Networks Guet et. al. Andrew Goodrich Charles Feng.

Combinatorial Synthesis of Genetic Networks Guet et. al. Andrew Goodrich Charles Feng.
Mukund Thattai NCBS Bangalore genetic networks in theory and practice.

Mukund Thattai NCBS Bangalore genetic networks in theory and practice.
Digital Signal Processing with Biomolecular Reactions Hua Jiang, Aleksandra Kharam, Marc Riedel, and Keshab Parhi Electrical and Computer Engineering University.

Digital Signal Processing with Biomolecular Reactions Hua Jiang, Aleksandra Kharam, Marc Riedel, and Keshab Parhi Electrical and Computer Engineering University.
Introductory Comments Regarding Hardware Description Languages.

Introductory Comments Regarding Hardware Description Languages.
ELEN 468 Lecture 121 ELEN 468 Advanced Logic Design Lecture 12 Synthesis of Combinational Logic I.

ELEN 468 Lecture 121 ELEN 468 Advanced Logic Design Lecture 12 Synthesis of Combinational Logic I.
Synthetic Gene Circuits Small, Middle-Sized and Huge Molecules Playing Together Within a Cell.

Synthetic Gene Circuits Small, Middle-Sized and Huge Molecules Playing Together Within a Cell.
Annual SERC Research Review - Student Presentation, October 5-6, 2011 1 Extending Model Based System Engineering to Utilize 3D Virtual Environments Peter.

Annual SERC Research Review - Student Presentation, October 5-6, Extending Model Based System Engineering to Utilize 3D Virtual Environments Peter.
Elements of Computing Systems, Nisan & Schocken, MIT Press, 2005, www.idc.ac.il/tecs, Introduction: Hello, World Below slide 1www.idc.ac.il/tecs Introduction:

Elements of Computing Systems, Nisan & Schocken, MIT Press, 2005, Introduction: Hello, World Below slide 1www.idc.ac.il/tecs Introduction:
Synthetic Mammalian Transgene Negative Autoregulation Harpreet Chawla April 2, 2015 Vinay Shimoga, Jacob White, Yi Li, Eduardo Sontag & Leonidas Bleris.

Synthetic Mammalian Transgene Negative Autoregulation Harpreet Chawla April 2, 2015 Vinay Shimoga, Jacob White, Yi Li, Eduardo Sontag & Leonidas Bleris.
First Institute of Higher Education in Modern China Since 1895 A World-class, Progressive, and Multidisciplinary Research University.

First Institute of Higher Education in Modern China Since 1895 A World-class, Progressive, and Multidisciplinary Research University.
Trigger design engineering tools. Data flow analysis Data flow analysis through the entire Trigger Processor allow us to refine the optimal architecture.

Trigger design engineering tools. Data flow analysis Data flow analysis through the entire Trigger Processor allow us to refine the optimal architecture.

Similar presentations

Ads by Google