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## Co-factors

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**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**My interests**Multiscale Physics Core theory challenges Network Centric, Pervasive, Embedded, Ubiquitous Systems Biology**Today:**Emphasis on motivation Core math challenges Technology Biology**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**Thanks to**• NSF • ARO/ICB • AFOSR • NIH/NIGMS • Boeing • DARPA • Lee Center for Advanced Networking (Caltech) • Hiroaki Kitano (ERATO) • Braun family**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**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**Terminology is “standard, conventional”**• Math • Biology • Technology • Other communities are important but I don’t easily “speak their language” Speak the “native” language**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**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)**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**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 )**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)**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**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**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**Random**Graphs and and Dynamic Networks ?**Math**Social networks? Math Biology Technology Ecology Social networks Two interesting subjects with little overlap Random Graphs Dynamic Networks**Math**Social networks? Math Biology Technology Ecology Social networks Today Random Graphs Dynamic Networks**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**Nonlinear/uncertain**hybrid/stochastic etc. Single Agent Complex networked systems Complexity of dynamics Flocking/synchronization consensus Multi-agent systems Complexity of interconnection**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)**“Emergent” complexity**• Simple question • Undecidable Simulations and conjectures but no “proofs’ • Chaos • Fractals Mandelbrot**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**Flocking/synchronization**consensus Multi-agent systems Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems Complexity of dynamics Single Agent Complexity of interconnection**Statistical Physics and**emergence of collective behavior Simulations and conjectures but no “proofs’**“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**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**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**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**Spectacular progress**Nonlinear/uncertain hybrid/stochastic etc. Single Agent Flocking/synchronization consensus Multi-agent systems Complexity of dynamics Complexity of interconnection**Open questions**Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems ? Complexity of dynamics Single Agent ? Flocking/synchronization consensus Multi-agent systems Complexity of interconnection**Unifying concepts**• Robustness • Constraints Ruthless oversimplification**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)**“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**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**Evolving evolvability?**“Random” Variation Structured Selection ?**Random variation is harmful, yet…**Variation Structured Selection Random, Small, Harmful**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**Structured variation can be good**Structured Selection Variation Structured, Large, Beneficial Architecture**Structured variation can be good**Not random Structured Selection Variation Structured, Large, Beneficial Architecture**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**Evolving evolvability?**Structured Selection Structured Variation “facilitated” “structured” “organized” Architecture**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**Lego hourglass**Diverse function Universal architectures Universal Control Diverse components • Bowties for flows • Hourglasses for control • Robust and evolvable • Architecture = protocols = constraints**Lego hourglass**Diverse function Universal Control control Diverse components assembly**Lego hourglass**Huge variety Standardized mechanisms Highly conserved control assembly Huge variety**Lego**Huge variety Limited environmental uncertainty needs minimal control Standard assembly Huge variety**Diverse**function Standard assembly Diverse components Variety of systems Variety of bricks Snap