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Design Principles in Synthetic Biology Chris Myers 1 , Nathan Barker - PowerPoint PPT Presentation

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Design Principles in Synthetic Biology Chris Myers 1 , Nathan Barker 2 , Hiroyuki Kuwahara 3 , Curtis Madsen 1 , Nam Nguyen 1 , Michael Samoilov 4 , and Adam Arkin 4 1 University of Utah 2 Southern Utah University 3 Microsoft Research, Trento, Italy

  1. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  2. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  3. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  4. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  5. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  6. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  7. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  8. Genetic Circuits CI Dimer CI Dimer Dimerization Degradation CII Protein CI Protein Translation Repression mRNA Transcription Activation Pre Pr DNA RNAP RNAP RNAP RNAP RNAP O E O R cI cII Operator Sites Promoters Genes C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  9. Logical Representation CI CII C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  10. Graphical Representation CI Pre Pr CII C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  11. Genetic Circuit Model (GCM) Provides a higher level of abstraction than SBML. Includes only important species and their influences upon each other. A GCM is a tuple � S , P , G , I , S d � where: S is a finite set of species; P is a finite set of promoters; G : P �→ 2 S maps promoters to sets of species; I ⊆ S × P ×{ a , r } is a finite set of influences; S d ⊆ S is a set of species that influence as dimers. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  12. GCM Graphical Representation A bipartite graph with species and promoters as the two types of nodes. Species are connected to promoters using influences I , and promoters are connected to species using function G . To simplify presentation, graphs shown using only species as nodes, edges are inferred using I and G , and edges are labeled with the promoter that links the species. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  13. Influences on the Same Promoter A B C A B P1 P1 C P1 c C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  14. Influences on the Same Promoter A B C A B P1 P1 C P1 c A C B C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  15. Influences on Different Promoters A C A B P1 P2 P1 c B C C P2 c C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  16. Influences on Different Promoters A C A B P1 P2 P1 c B C C P2 c A C B C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  17. GCM Parameters Parameter Sym Structure Value Units n s molecule Initial species count species 0 K d 1 Dimerization equilibrium species .05 molecule k d 1 Degradation rate species .0075 sec n g molecule Initial promoter count promoter 2 n p molecule Stoichiometry of production promoter 10 n c molecule Degree of cooperativity promoter 2 K o 1 RNAP binding equilibrium promoter .033 molecule k o 1 Open complex production rate promoter .05 sec k b 1 Basal production rate promoter .0001 sec k a 1 Activated production rate promoter .25 sec K r 1 Repression binding equilibrium influence .5 molecule nc K a 1 Activation binding equilibrium influence .0033 molecule ( nc + 1 ) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  18. GCM versus SBML Representation CI Pre Pr CII C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  19. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  20. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  21. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  22. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  23. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  24. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  25. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  26. SBML Example C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  27. Synthesizing SBML from a GCM Representation Create degradation reactions Create open complex formation reactions Create dimerization reactions Create repression reactions Create activation reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  28. Degradation Reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  29. Open Complex Formation Reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  30. Dimerization Reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  31. Repression Reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  32. Activation Reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  33. Complete SBML Model C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  34. Classical Chemical Kinetics Uses ordinary differential equations (ODE) to represent the system to be analyzed, and it assumes: A system is well-stirred. Number of molecules in a cell is high. Concentrations can be viewed as continuous variables. Reactions occur continuously and deterministically. Genetic circuits involve small molecule counts. Gene expression can have substantial fluctuations. ODEs do not capture non-deterministic behavior. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  35. Stochastic Chemical Kinetics To more accurately predict the temporal behavior of genetic circuits, stochastic chemical kinetics formalism can be used. Probabilistically predicts the dynamics of biochemical systems. Describes the time evolution of a system as a discrete-state jump Markov process governed by the chemical master equation (CME). Can simulate it using Gillespie’s Stochastic Simulation Algorithm (SSA). It exactly tracks the quantities of each molecular species, and treats each reaction as a separate random event. Only practical for small systems with no major time-scale separations. Abstraction is essential for efficient analysis of any realistic system. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  36. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  37. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  38. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  39. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  40. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  41. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  42. � � � � � � � Automatic Abstraction Markov � Abstracted Reaction Reaction-based State-based � SAC Reaction Chain Model Abstraction Abstraction Model Analysis Model �� �� Stochastic � Results Simulation Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  43. Dimerization Reduction C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  44. Dimerization Reduction C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  45. Operator Site Reduction (PR) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  46. Operator Site Reduction (PR) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  47. Operator Site Reduction (PRE) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  48. Operator Site Reduction (PRE) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  49. Similar Reaction Combination C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  50. Modifier Constant Propagation C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  51. Final SBML Model 10 species and 10 reactions reduced to 2 species and 4 reactions C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  52. BioSim: Genetic Circuit Editor http://www.async.ece.utah.edu/BioSim/ C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  53. BioSim: SBML Editor http://www.async.ece.utah.edu/BioSim/ C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  54. BioSim: Simulator http://www.async.ece.utah.edu/BioSim/ C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  55. BioSim: Parameter Editor http://www.async.ece.utah.edu/BioSim/ C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  56. BioSim: Graph Editor http://www.async.ece.utah.edu/BioSim/ C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  57. GCM Advantages Greatly increases the speed of model development and reduces the number of errors in the resulting models. Allows efficient exploration of the effects of parameter variation. Constrains SBML model such that it can be more easily abstracted resulting in substantial improvement in simulation time. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  58. Genetic Muller C-Element A A B C’ 0 0 0 C’ C 0 1 C 1 0 C B 1 1 1 C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  59. Toggle Switch C-Element (Genetic Circuit) A D X P1 d x B E X Y A C X Y S Q B P2 P3 e x y D R F A D Z F P7 f E F Z B E P8 P4 f z C Z Y P5 P6 c y z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  60. Toggle Switch C-Element (GCM) A D X P1 d x B E X Y P2 P3 e x y D F P7 f E F Z P8 P4 f z C Z Y P5 P6 c y z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  61. Toggle Switch C-Element (SBML) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  62. Toggle Switch C-Element (Abstracted) Reduced from 34 species and 31 reactions to 9 species and 15 reactions. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  63. Toggle Switch C-Element (Simulation) Simulation time improved from 312 seconds to 20 seconds. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  64. Majority Gate C-Element (Genetic Circuit) A X B Y E C D Z X A Y D P1 P5 x y d Z X B E D C P2 P4 P7 P8 z x d e c Y Z D D P3 P6 y z d C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  65. Majority Gate C-Element (GCM) X A Y D P1 P5 x y d Z X B E D C P2 P4 P7 P8 z x d e c Y Z D D P3 P6 y z d C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  66. Majority Gate C-Element (Simulation) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  67. Speed-Independent C-Element (Genetic Circuit) A S1 S2 B S3 S4 C S4 A X S1 S2 S3 P1 P4 P5 P6 s4 x s1 s2 s3 B S4 Y S3 S1 Z S2 P2 P7 P8 s4 y s1 s2 z S2 S4 S3 Z C S4 P3 P9 P10 s3 z c s4 C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  68. Speed-Independent C-Element (GCM) S4 A X S1 S2 S3 P1 P4 P5 P6 s4 x s1 s2 s3 B S4 Y S3 S1 Z S2 P2 P7 P8 s4 y s1 s2 z S2 S4 S3 Z C S4 P3 P9 P10 s3 z c s4 C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  69. Speed-Independent C-Element (Simulation) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  70. Ordinary Differential Equation Analysis Use Law of Mass Action to derive an ODE model. Study behavior of our model at steady state. Analyze nullclines to characterize the gate. C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  71. ODE Analysis: Nullclines for Toggle C-Element Toggle, Inputs low dY=0 120 dZ=0 100 80 Y 60 40 20 Stable 0 0 20 40 60 80 100 120 Z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  72. ODE Analysis: Nullclines for Toggle C-Element Toggle, Inputs Mixed Toggle, Inputs Mixed dY=0 dY=0 120 120 dZ=0 dZ=0 100 100 80 80 Stable Y Y 60 60 40 40 20 20 Unstable Stable 0 0 0 0 20 20 40 40 60 60 80 80 100 100 120 120 Z Z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  73. ODE Analysis: Nullclines for Toggle C-Element Toggle, Inputs High Stable dY=0 120 dZ=0 100 80 Y 60 40 20 0 0 20 40 60 80 100 120 Z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  74. ODE Analysis: Nullclines for Toggle C-Element Toggle, Inputs Mixed Toggle, Inputs Mixed dY=0 dY=0 120 120 dZ=0 dZ=0 100 100 80 80 Stable Y Y 60 60 40 40 20 20 Unstable Stable 0 0 0 0 20 20 40 40 60 60 80 80 100 100 120 120 Z Z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  75. Stochastic Simulation: State Change from Low to High Toggle, Inputs Mixed dY=0 120 dZ=0 100 80 Stable Y 60 ? 40 20 Unstable Stable 0 0 20 40 60 80 100 120 Z C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

  76. Stochastic Simulation: State Change from Low to High Low to High 0.03 maj−heat−high maj−light−high tog−heat−high 0.025 tog−light−high si−heat−high si−light−high 0.02 Failure Rate 0.015 0.01 0.005 0 0 500 1000 1500 2000 Time (s) C. Myers et al. (U. of Utah) Design Principles in Synthetic Biology IMA Workshop / April 24, 2008

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