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Co-factors PowerPoint Presentation

Co-factors

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Co-factors

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  1. Ligand In Receptor GDP GTP  Out  G G GDP GTP P In RGS Polymerization and complex assembly Autocatalytic feedback Taxis and transport Proteins Complexity and Core metabolism Sugars Catabolism Amino Acids Nucleotides Precursors Nutrients Trans* Fatty acids Genes Co-factors Carriers Architecture DNA replication John Doyle John G Braun Professor Control and Dynamical Systems, BioEng, and ElecEng Caltech www.cds.caltech.edu/~doyle

  2. My interests Multiscale Physics Core theory challenges Network Centric, Pervasive, Embedded, Ubiquitous Systems Biology

  3. Today: Emphasis on motivation Core math challenges Technology Biology

  4. Collaborators and contributors(partial list) Biology:Csete,Yi, El-Samad, Khammash, Tanaka, Arkin, Savageau, Simon, AfCS, Kurata, Smolke, Gross, Kitano, Hucka, Sauro, Finney,Bolouri, Gillespie, Petzold, F Doyle, Stelling, Caporale,… Theory:Parrilo, Carlson, Murray,Vinnicombe, Paganini, Mitra Papachristodoulou, Prajna, Goncalves, Fazel, Liu,Lall, D’Andrea, Jadbabaie,Dahleh, Martins, Recht,many more current and former students, … Web/Internet: Li, Alderson, Chen, Low, Willinger,Kelly, Zhu,Yu, Wang, Chandy, … Turbulence: Bamieh, Bobba, McKeown,Gharib,Marsden, … Physics:Sandberg,Mabuchi, Doherty, Barahona, Reynolds, Disturbance ecology: Moritz, Carlson,… Finance:Martinez, Primbs, Yamada, Giannelli,… Current Caltech Former Caltech Longterm Visitor Other

  5. Thanks to • NSF • ARO/ICB • AFOSR • NIH/NIGMS • Boeing • DARPA • Lee Center for Advanced Networking (Caltech) • Hiroaki Kitano (ERATO) • Braun family

  6. Background progress • Spectacular progress, both depth and breadth • Biological networks • Technological networks • Mathematical foundations • Remarkably consistent, convergent, coherent • Role of protocols, architecture, feedback, and dynamics • Yet seemingly persistent errors and confusion both within science between science and public & policy

  7. Terminology is “standard, conventional” • Math: dynamic, (non)random, (non)linear, conjecture, theorem, proof, evidence, etc. • Biology: DNA, RNA, protein, allostery, covalent, precursor, carrier, kinase, evolution, etc. • Technology: router, transistor, TCP/IP, protocol, hardware, software, verification, robustness, scalability, etc

  8. Terminology is “standard, conventional” • Math • Biology • Technology • Other communities are important but I don’t easily “speak their language” Speak the “native” language

  9. Some ambiguities Some words are widely used but with substantial inconsistencies • “Complex, emergent, irreducible,” etc • “Design, architecture, evolvability, aesthetic,” etc. I will tend to math and/or tech usage

  10. Background progress: Biological networks (With molecular biology  details of components) + systems biology • Organizational principles are increasingly apparent • Beginning to see principles of architecture (as well as components and circuits)

  11. Background progress: Technological networks • Complexity of advanced technology  biology • Components extremely different • Yet, striking convergence at network level: • Architecture as constraints • Layering and protocols • Feedback control

  12. Background progress: Mathematics New mathematical frameworks suggests • apparent network-level evolutionary convergence • within/between biology/technology is not accidental But follows necessarily from universal requirements: • efficient, • adaptive, • evolvable, • robust (to both environment and component )

  13. Background progress: Mathematics New mathematical frameworks suggests • apparent network-level evolutionary convergence • within/between biology/technology is not accidental But follows necessarily from universal requirements: efficient, adaptive, evolvable, robust For example (which we won’t talk about much today): • New theories of Internet and related networking technologies confirm engineering intuition (Kelly, Low, many others… See IPAM 2002 program) • Also lead to test and deployment of new protocols for high performance networking (e.g. FAST TCP)

  14. Background progress: Mathematics • Blends (from engineering) theories from • optimization, • control, • information, and • computational complexity • with diverse elements in areas of mathematics (e.g. operator theory and algebraic geometry) not traditionally thought of as applied

  15. Background progress • Spectacular progress, both depth and breadth • Biological networks • Technological networks • Theoretical foundations • Remarkably consistent, convergent, coherent • Role of protocols, architecture, feedback, and dynamics • Yet seemingly persistent errors and confusion both within science between science and public & policy

  16. Persistent errors • Errors and confusion both within science and between science and public & policy • Evolution, stem cells, global warming,… • Creationism, irreducible complexity and “intelligent design” • “New sciences of…”, edge-of-chaos, self-organized criticality, scale-free networks, etc etc… • Consensus among experts conflicts with “mainstream” (e.g. faith-based) views • Mixed progress in “converting” the mainstream

  17. Random Graphs and and Dynamic Networks ?

  18. Math Social networks? Math Biology Technology Ecology Social networks Two interesting subjects with little overlap Random Graphs Dynamic Networks

  19. Math Social networks? Math Biology Technology Ecology Social networks Today Random Graphs Dynamic Networks

  20. Networked dynamical systems Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems Single Agent Flocking/synchronization consensus Multi-agent systems Complexity of dynamics Complexity of interconnection

  21. Nonlinear/uncertain hybrid/stochastic etc. Single Agent Complex networked systems Complexity of dynamics Flocking/synchronization consensus Multi-agent systems Complexity of interconnection

  22. Bode Shannon d d e=d-u e=d-u Disturbance - - u u Plant Capacity C Channel Decode Encode Control Incompatible assumptions (for 50+ years). • Hard bounds • Achievable (assumptions) • Solution decomposable (assumptions)

  23. “Emergent” complexity • Simple question • Undecidable Simulations and conjectures but no “proofs’ • Chaos • Fractals Mandelbrot

  24. Networked dynamical systems Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems Single Agent Flocking/synchronization consensus Multi-agent systems Complexity of dynamics Complexity of interconnection

  25. Flocking/synchronization consensus Multi-agent systems Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems Complexity of dynamics Single Agent Complexity of interconnection

  26. Working systems but no “proofs’

  27. Statistical Physics and emergence of collective behavior Simulations and conjectures but no “proofs’

  28. “FAST” TCP/AQM theory • Arbitrarily complex network • Topology • Number of routers and hosts • Nonlinear • Delays Routers • Short proof • Global stability • Equilibrium optimizes aggregate user utility Hosts Papachristodoulou, Li packets

  29. Layering as optimization decomposition application transport network link physical Application: utility Phy: power IP: routing Link: scheduling • Each layer is abstracted as an optimization problem • Operation of a layer is a distributed solution • Results of one problem (layer) are parameters of others • Operate at different timescales

  30. Examples application transport network link physical Optimal web layer: Zhu, Yu, Doyle ’01 HTTP/TCP: Chang, Liu ’04 TCP: Kelly, Maulloo, Tan ’98, …… TCP/IP: Wang et al ’05, …… TCP/MAC: Chen et al ’05, …… TCP/power control: Xiao et al ’01, Chiang ’04, …… Rate control/routing/scheduling: Eryilmax et al ’05, Lin et al ’05, Neely, et al ’05, Stolyar ’05, this paper detailed survey in Proc. of IEEE, 2007

  31. I2LSR, SC2004 Bandwidth Challenge “FAST” TCP/AQM implementation OC48 Harvey Newman’s group, Caltech http://dnae.home.cern.ch/dnae/lsr4-nov04 OC192 November 8, 2004 Caltech and CERN transferred • 2,881 GBytes in one hour (6.86Gbps) • between Geneva - US - Geneva (25,280 km) • through LHCnet/DataTag, Abilene and CENIC backbones • using 18 FAST TCP streams

  32. Spectacular progress Nonlinear/uncertain hybrid/stochastic etc. Single Agent Flocking/synchronization consensus Multi-agent systems Complexity of dynamics Complexity of interconnection

  33. Open questions Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems ? Complexity of dynamics Single Agent ? Flocking/synchronization consensus Multi-agent systems Complexity of interconnection

  34. Unifying concepts • Robustness • Constraints Ruthless oversimplification

  35. Human complexity Robust Yet Fragile • Efficient, flexible metabolism • Complex development and • Immune systems • Regeneration & renewal • Complex societies • Advanced technologies • Obesity and diabetes • Rich microbe ecosystem • Inflammation, Auto-Im. • Cancer • Epidemics, war, … • Catastrophic failures • Evolved mechanisms for robustness allow for, even facilitate, novel, severe fragilities elsewhere • often involving hijacking/exploiting the same mechanism • There are hard constraints (i.e. theorems with proofs)

  36. “Constraints” as unifying concept • “Robust yet fragile” is a hard constraint • Architecture: “Constraints that deconstrain” • Complexity of systems: due to constraints on robustness/evolvability rather than minimal functionality

  37. Accessible Biology Authors Savageau Kirschner and Gerhart Caporale da Silva and Williams Woese Wachtershauser de Duve Constraints in biology Networks and systems Physico- Chemical Components Biology as technology

  38. Evolving evolvability? “Random” Variation Structured Selection ?

  39. Random variation is harmful, yet… Variation Structured Selection Random, Small, Harmful

  40. Polymerization and complex assembly Autocatalytic feedback Taxis and transport Proteins Core metabolism Sugars Catabolism Amino Acids Nucleotides Precursors Nutrients Trans* Fatty acids Genes Co-factors Carriers DNA replication Architecture E. coli genome

  41. Structured variation can be good Structured Selection Variation Structured, Large, Beneficial Architecture

  42. Structured variation can be good Not random Structured Selection Variation Structured, Large, Beneficial Architecture

  43. Structured variation can be good • Robust architectures facilitate change: • Small genotype  large, functional phenotype  • (Wolves  Dogs) regulatory regions • Large (but functional) genotype  are facilitated • (Antibiotic resistance) Horizontal gene transfer Structured Selection Variation Structured, Large, Beneficial Architecture

  44. Evolving evolvability? Structured Selection Structured Variation “facilitated” “structured” “organized” Architecture

  45. universal carriers fan-out of diverse outputs fan-in of diverse inputs Universal architectures Diverse function • Bowties for flows • Hourglasses for control • Robust and evolvable • Architecture = protocols = constraints Universal Control Diverse components

  46. Lego hourglass Diverse function Universal architectures Universal Control Diverse components • Bowties for flows • Hourglasses for control • Robust and evolvable • Architecture = protocols = constraints

  47. Lego hourglass Diverse function Universal Control control Diverse components assembly

  48. Lego hourglass Huge variety Standardized mechanisms Highly conserved control assembly Huge variety

  49. Lego Huge variety Limited environmental uncertainty needs minimal control Standard assembly Huge variety

  50. Diverse function Standard assembly Diverse components Variety of systems Variety of bricks Snap