1. http://www.cds.caltech.edu/~doyle/shortcourse.htm Systems Biology Shortcourse May 21-24 Winnett Lounge, Caltech Speakers: Adam Arkin (UC Berkeley), Frank Doyle (UCSB), Drew Endy (MIT), Dan Gillespie (Caltech), Michael Savageau (UC Davis) Organized by John Doyle (Caltech). There is no registration or fees. Note: Friday 4pm talk by Adam Arkin in Beckman Institute Auditorium.

2. Collaborators and contributors (partial list) Theory: Parrilo, Carlson, Paganini, Papachristodoulo, Prajna, Goncalves, Fazel, Lall, DAndrea, Jadbabaie, many current and former students, … Web/Internet: Low, Willinger, Vinnicombe, Kelly, Zhu,Yu, Wang, Chandy, Effros, … Biology: Csete,Yi, Tanaka, Arkin, Savageau, Simon, AfCS, Kurata, Khammash, El-Samad, Gross, Bolouri, Kitano, Hucka, Sauro, Finney, … Turbulence: Bamieh, Dahleh, Bobba, Gharib, Marsden, … Physics: Mabuchi, Doherty, Barahona, Reynolds, Asimakapoulos,… Engineering CAD: Ortiz, Murray, Schroder, Burdick, … Disturbance ecology: Moritz, Carlson, Robert, … Finance: Martinez, Primbs, Yamada, Giannelli,… Caltech faculty Other Caltech Other

3. Transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Whole cell metabolism

4. Metabolite Enzyme +Regulation Autocatalysis

5. Metabolite Enzyme +Regulation Autocatalysis

6. Metabolite Enzyme + - +

7. Stoichiometry or mass and energy balance Nutrients Products Interna l

8. Core metabolism

9. Transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Whole cell metabolism

10. transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Nested bowties

11. Polymerization and assembly transport Core metabolism Nested bowties Our first universal architecture

12. The core metabolism bowtie Nutrients Products

13. Energy and reducing Fatty acids Sugars Nucleotides Amino Acids Biosynthesis Catabolism Carriers and Precursor Metabolites Cartoon metabolism

14. Catabolism Carriers and Precursor Metabolites The metabolism bowtie protocol Energy and reducing Fatty acids Sugars Nucleotides Amino Acids Nutrients Products Catabolism Synthesis

15. Core: special purpose enzymes controlled by competitive inhibition and allostery Edges: general purpose polymerases and machines controlled by regulated recruitment Uncertain

16. Core: Highly efficient Edges: Robustness and flexibility Uncertain

17. Almost everything complex is made this way: Cars, planes, buildings, power, fuel, laptops,… This cartoon is pure protocol.

18. Collect and import raw materials Common currencies and building blocks Complex assembly Manufacturing and metabolism Collect and import raw materials Common currencies and building blocks Complex assembly Taxis and transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback

19. Variety of producers Electric power

20. Variety of consumers Electric power

21. Variety of consumers Variety of producers Energy carriers 110 V, 60 Hz AC (230V, 50 Hz AC) Gasoline ATP, glucose, etc Proton motive force

22. Complex assembly Raw materials Complex assembly Raw materials Building blocks

23. Collect and import raw materials Common currencies and building blocks Complex assembly Steel manufacturing

24. Variety of consumers Variety of producers Energy carriers transport assemblymetabolism Core: special purpose machines controlled by allostery

25. Variety of consumers Variety of producers Energy carriers transport assemblymetabolism Edges: general purpose machines controlled by regulated recruitment

26. Variety of consumers Variety of producers Energy carriers transport assemblymetabolism Robust and evolvable

27. Variety of consumers Variety of producers Energy carriers transport assemblymetabolism Fragile and hard to change

28. Variety of consumers Variety of producers Energy carriers transport assemblymetabolism Preserved by selection on three levels: 1.Fragile to change (short term) 2.Facilitates robustness elsewhere (short term) 3.Facilitates evolution (long term)

29. Modules and protocols Much confusion surrounds these terms Biologists already understand the important distinction Most of basic sciences doesnt

30. Modules and protocols in experiments Modules: components of experiments Protocols: rules or recipes by which the modules interact This generalizes to most important situations Important distinction in experiments Even more important in understanding the complexity of biological networks

31. Modules and protocols example Suppose some specific experimental protocol has a step that requires the use of a PCR machine module. The PCR machine in turn implements a complex protocol with its own modules. Thus protocols and modules are hierarchically nested. A nested collection of protocols/modules is called an architecture or protocol suite.

32. Modules and protocols example Consider this laptop/projector combination. The modules include software, hardware, and connectors. The protocols are the rules by which these modules must interact. Hardware modules change between talks Within talks slides change, not hardware Robust and evolvable yet fragile

33. Modules and protocols example Consider this laptop/projector combination. The modules include software, hardware, and connectors. The protocols are the rules by which these modules must interact. Hardware modules change between talks Within talks slides change, not hardware Robust and evolvable yet fragile

34. Varied components Varied systems Robust Mesoscale The LEGO connector protocol

35. Software Hardware Early computing Analog substrate Various functionality Digital

36. Software Hardware Applications Operating System Modern Computing

37. Software Hardware Applications Operating System Modern Computing

38. Modules and protocols Protocols and modules are complementary (dual) notions Primitive technologies = modules are more important than protocols Advanced technologies = protocols are at least as important Even bacteria are advanced technology

39. Reductionism and protocols Reductionism = modules are more important than protocols Usually: Huh? Whats a protocol? Systems approach: Protocols are as important as modules

40. Necessity or frozen accident? Laws are absolute necessity. Conjecture: Protocols in biology are largely necessary. (More so than in engineering!) Modules??? Appear to be more of a mix of necessity and accident.

41. Necessity or frozen accident? Conservation laws are necessary. Bowtie protocols are essentially necessary if robustness and efficiency are required. Conjecture: It is necessary that there is an energy carrier, it may not be necessary that it be ATP.

42. Conjectures on laws and protocols The important laws governing biological complexity have yet to be fully articulated Biology has highly organized dynamics using protocol suites Both are true for advanced technologies

43. Taxis and transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Nested bowtie and hourglass Enzymes are modules. Bowtie architectures is a protocol. Conservation of energy and moiety is a law.

44. essential : 230 nonessential:2373 unknown:1804 total:4407 http://www.shigen.nig.ac.jp/ecoli/pec

45. Autocatalytic feedback Regulatory feedback transport assemblymetabolism

46. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Knockouts often lose robustness, not minimal functionality Knockouts often lose robustness, not minimal functionality Knockouts often lethal

47. Brakes Airbags Seatbelts MirrorsWipers Headlights Steering GPS Radio Shifting Traction control Anti-skid Electronic ignition Electronic fuel injection Temperature control Cruise control Bumpers Fenders Suspension (control) Seats

48. Brakes Airbags Seatbelts MirrorsWipers Headlights Steering GPS Radio Shifting Traction control Anti-skid Electronic ignition Electronic fuel injection Temperature control Cruise control Bumpers Fenders Suspension (control) Seats Knockouts often lose robustness, not minimal functionality Knockouts often lose robustness, not minimal functionality Knockouts often lethal

49. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Supplies Materials & Energy Supplies Materials & Energy Supplies Robustness Supplies Robustness Complexity Robustness Complexity

50. Autocatalytic feedback Regulatory feedback transport assemblymetabolism If feedback regulation is the dominant protocol, what are the laws constraining whats possible?

51. Autocatalytic feedback Regulatory feedback transport assemblymetabolism A historical aside: These systems are not at the edge-of-chaos, self-organized critical, scale-free, at an order- disorder transition, etc Not only are they as opposite from this as can possibly be (an observational fact)… But also, it is provably impossible for robust systems to have it otherwise (a theoretical assertion) The facts are easily checked, what is the theoretical foundation? A historical aside: These systems are not at the edge-of-chaos, self-organized critical, scale-free, at an order- disorder transition, etc Not only are they as opposite from this as can possibly be (an observational fact)… But also, it is provably impossible for robust systems to have it otherwise (a theoretical assertion) The facts are easily checked, what is the theoretical foundation?

52. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Supplies Materials & Energy Supplies Materials & Energy Supplies Robustness Supplies Robustness What are the laws of robustness?

53. Transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Whole cell metabolism

54. Metabolite Enzyme +Regulation Autocatalysis

55. Metabolite Enzyme +Regulation Autocatalysis

56. Metabolite Enzyme + - +

57. perturbation Product inhibition Yi, Ingalls, Goncalves, Sauro

58. h = [0 1 2 3] 05101520 0.8 0.85 0.9 0.95 1 1.05 Time (minutes) [ATP] h = 2 h = 1 h = 0 Step increase in demand for ATP. h = 3

59. 05101520 Time h = 3 h = 2 h = 1 h = 0 Tighter steady-state regulation Transients, Oscillations Higher feedback gain

60. 05101520 0.8 0.85 0.9 0.95 1 1.05 Time (minutes) [ATP] h = 3 h = 0 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 3 h = 0 Spectrum Time response Robust Yet fragile

61. 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 3 h = 0 Robust Yet fragile

62. 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 0 Robust Yet fragile

63. 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 3 h = 2 h = 1 h = 0 Tighter steady-state regulation Transients, Oscillations Theorem

64. log|S | Tighter regulation Transients, Oscillations Biological complexity is dominated by the evolution of mechanisms to more finely tune this robustness/fragility tradeoff. This tradeoff is a law.

65. Product inhibition is a protocol. log|S | This tradeoff is a law.

66. Product inhibition is a protocol. log|S | This tradeoff is a law. PFK and ATP are modules.

67. log|S | Conservation of fragility

68. Robust Fragile Uncertainty Diseases of complexity Parasites Cancer Epidemics Auto-immune disease Complex development Regeneration/renewal Complex societies Immune response

69. X n+1 X0X0 X1X1 XiXi XnXn ……Error log|S | We have a proof of this.

70. Robust Fragile Uncertainty Parasites Cancer Epidemics Auto-immune disease Complex development Regeneration/renewal Complex societies Immune response This is a cartoon. We have no proof of this. Yet.

71. Robust Fragile Uncertainty Parasites Cancer Epidemics Auto-immune disease Immune response Development Regeneration renewal Societies Why should any biologists care about this? How does it effect what can be done to understand complex biological networks?

72. Robust Fragile Uncertainty Modeling robust yet fragile systems Required model complexity May need great detail here And much less detail here.

73. Robust Fragile Uncertainty Required model complexity More detail. Less detail. Robust (fragile) to perturbations in components and environment Robust (fragile) to errors and simplifications in modeling

74. 05101520 Time h = 3 h = 2 h = 1 h = 0 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 3 h = 2 h = 1 h = 0 Tighter steady-state regulation Transients, Oscillations

75. Metabolite Enzyme +Regulation Autocatalysis Energy and materials

76. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Even though autocatalytic feedback contributes relatively modestly to complexity, it has a huge indirect impact on regulatory complexity.

77. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Autocatalysis is everywhere in human and natural systems as well as biology Make energy, materials, and machines to make energy, materials, and machines to make … Consumers are investors are labor… Autocatalysis is everywhere in human and natural systems as well as biology Make energy, materials, and machines to make energy, materials, and machines to make … Consumers are investors are labor…

78. 05101520 Time h = 3 h = 2 h = 1 h = 0 0246810 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Frequency Log(Sn/S0) h = 3 h = 2 h = 1 h = 0 Tighter steady-state regulation Transients, Oscillations Regulatory feedback only

79. Add autocatalytic feedback more

80. Add autocatalytic feedback Transients, Oscillations

81. Add more regulator feedback

82. More instability aggravates

83. Control demo

84. Autocatalytic feedback Regulatory feedback transport assemblymetabolism Enzymes are modules. Bowtie architectures with product inhibition is a protocol suite. Conservation of energy, moiety, and fragility are laws.

85. Taxis and transport Polymerization and assembly Core metabolism Autocatalytic and regulatory feedback Nested bowtie and hourglass Enzymes are modules. Bowtie architectures is a protocol. Conservation of energy and moiety is a law.

86. Key themes 1.Multiscale and large-scale stochastic simulation is an essential technology for systems biology. 2.Simulation alone is not scalable to larger network problems because complex, uncertain systems need an exponentially large number of simulations to answer biologically meaningful questions. 3.There are fundamental laws governing the organization of biological networks.

87. Hypotheses 1.Multiscale and large-scale stochastic simulation. Gillespie + Petzold for stiff stochastic systems. 2.Simulation alone is not scalable. Automated scalable inference using SOSTOOLS. 3.There are fundamental laws governing the organization of biological networks. Without exploiting these, the complexity is overwhelming.

88. A coherent foundation for a general understanding of highly evolved complexity Recently, there has been a remarkable convergences.

89. Molecular biology has catalogued cellular components, and network structure is becoming more apparent. Biology

90. Advanced technologies are producing networks approaching biological levels of complexity (which is hidden to the user). Biology Advanced Technology

91. Biology Advanced Technology Math New mathematics provides for the first time a coherent theoretical framework for complex networks (but not yet an accessible one).

92. A coherent foundation for a general understanding of highly evolved complexity Biology Advanced Technology Math After many false starts.

93. Complementary ways to tell this story: 1.Give lots of examples from biology and technology 2.Prove relevant theorems 3.Deliver useful software tools Biology Advanced Technology Math

94. Today: an attempt to distill an accessible message from enormous amount of detail Focus on universal laws that transcend details Minimize math, maximize examples Provide broader context for the rest of the shortcourse Biology Advanced Technology Math

95. This week: Case studies in microbial signaling and regulation networks Will attempt to put details into broader context Saturday will consider computational challenges Biology Advanced Technology Math

96. NP coNP P easy Hard Problems

97. NP coNP P Controls Communications Economics Dynamical Systems Physics Algorithms Hard Problems

98. P Controls Communications Economics Dynamical Systems Physics Algorithms Domain-specific assumptions Enormously successful Handcrafted theories Incompatible assumptions Tower of Babel where even experts cannot communicate Unified theories failed New challenges unmet

99. NP coNP P Controls Communications Economics Dynamical Systems Physics Algorithms Hard Problems Internet

100. NP coNP P Controls Communications Economics Dynamical Systems Physics Algorithms Hard Problems Biology Internet

101. Biology and advanced technology Biology –Integrates control, communications, computing –Into distributed control systems –Built at the molecular level Advanced technologies will increasingly do the same We need new theory and math, plus unprecedented connection between systems and devices Two challenges for greater integration: –Unified theory of systems –Multiscale: from devices to systems

102. NP coNP P Hard Problems Controls Communications Economics Dynamical Systems Physics Algorithms Goal Internet Biology Unified Theory

103. NP coNP P Controls Communications Economics Dynamical Systems Physics Algorithms Hard Problems Biology Internet