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Synthetic Biology: A New Application Area for Design Automation Research Chris Myers University of Utah Carnegie Mellon University December 10, 2009 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University James Watson Biology

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  6. Francis Crick DNA makes RNA, RNA makes protein, and proteins make us. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  7. Johann Von Neumann The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  8. Scott Adams There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards, or crystal balls. Collectively, these methods are known as “nutty methods.” Or you can put well-researched facts into sophisticated computer models, more commonly referred to as “a complete waste of time.” Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  9. Genetic Circuit Model (GCM) Provides a higher level of abstraction than SBML. Includes only important species and their influences upon each other. GCMs also include structural constructs that allow us to connect GCMs for separate modules through species ports. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  10. A Genetic Not Gate A A C P1 C P1 c A C Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  11. A Genetic Nor Gate A B C A B P1 P1 C P1 c A C B Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  12. A Genetic Nand Gate A C A B P1 P2 P1 c B C C P2 c A C B Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  13. A Genetic Oscillator CI Dimer CI Dimer CI CII Protein CI Protein Pre Pr CII Pre Pr O E O R cI cII CI CII Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  14. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  15. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  16. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  17. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  18. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  19. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  20. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  21. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  22. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  23. SBML: Main Elements Species Global parameters (ex. k1=0.1) Reactions Reactants Products Modifiers Stoichiometry Reversible Kinetic laws Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  24. Synthesizing SBML from a GCM Representation Create degradation reactions Create open complex formation reactions Create dimerization reactions Create repression reactions Create activation reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  25. GCM Example CI Pre Pr CII Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  26. Degradation Reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  27. Open Complex Formation Reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  28. Dimerization Reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  29. Repression Reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  30. Activation Reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  31. Complete SBML Model Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  32. Classical Chemical Kinetics Uses ordinary differential equations (ODE) to represent the system to be analyzed, and it assumes: Molecule counts are high, so concentrations can be continuous variables. Reactions occur continuously and deterministically. Genetic circuits have: Small molecule counts which must be considered as discrete variables. Gene expression reactions that occur sporadically. ODEs do not capture non-deterministic behavior. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  33. Richard Feynmann A philosopher once said “It is necessary for the very existence of science that the same conditions always produce the same results.” Well, they do not. You set up the circumstances, with the same conditions every time, and you cannot predict behind which hole you will see the electron. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  34. NYTimes: Expressing Our Individuality, the Way E. Coli Do Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  35. Rainbow and CC Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  36. Stochastic Chemical Kinetics To more accurately predict the temporal behavior of genetic circuits, stochastic chemical kinetics formalism can be used. Use Gillespie’s Stochastic Simulation Algorithm which 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. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  43. � � � � � � � 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. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  44. Dimerization Reduction Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  45. Dimerization Reduction Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  46. Operator Site Reduction (PR) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  47. Operator Site Reduction (PR) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  48. Operator Site Reduction (PRE) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  49. Operator Site Reduction (PRE) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  50. Similar Reaction Combination Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  51. Modifier Constant Propagation Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  52. Final SBML Model 10 species and 10 reactions reduced to 2 species and 4 reactions Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  53. 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. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  54. iBioSim: The Intelligent Biological Simulator Project management support. GCM Editor - creates Genetic Circuit Models (GCM). SBML Editor - creates models using the Systems Biology Markup Language (SBML). reb2sac - abstraction-based ODE, Monte Carlo, and Markov analysis. TSD Graph Editor - visualizes time series data (TSD). Probability Graph Editor - visualizes probability data. GeneNet - learns GCMs from TSD. Myers et al., Bioinformatics (2009) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  55. iBioSim: Genetic Circuit Editor Myers et al., Bioinformatics (2009) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  56. iBioSim: SBML Editor Myers et al., Bioinformatics (2009) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  57. iBioSim: Analysis Engine Myers et al., Bioinformatics (2009) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  58. ODE Results for the Simple Genetic Oscillator Comparison of ODE to SSA Results 6 5 6 0 5 5 5 0 Number of molecules 4 5 4 0 3 5 3 0 2 5 2 0 1 5 1 0 5 0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 Time (s) CI_total (ODE) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  59. SSA Results for the Simple Genetic Oscillator Comparison of ODE to SSA Results 150 140 130 120 110 Number of molecules 100 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 Time (s) CI_total (ODE) CI_total (SSA) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  60. Genetic Muller C-Element A A B C 0 0 0 C 0 1 C C 1 0 C B 1 1 1 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  61. 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 Nguyen et al., 13th Symposium on Async. Ckts. & Sys., 2007 ( best paper ) Nguyen et al., to appear in the Journal of Theoretical Biology Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  62. 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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  63. Toggle Switch C-Element (SBML) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  64. Toggle Switch C-Element (Abstracted) Reduced from 34 species and 31 reactions to 9 species and 15 reactions. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  65. Toggle Switch C-Element (Simulation) Simulation time improved from 312 seconds to 20 seconds. Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  66. 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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  68. Genetic C-element Failures 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 Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

  69. Comparison of Failure Rates for the C-element Designs Failure Rate for Each C-Element Design 0.02 Majority Gate (High to Low) Speed-Independent (High to Low) Toggle Switch (High to Low) 0.015 Failure Rate 0.01 0.005 0 0 500 1000 1500 2000 Time (s) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

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