Universal laws and architecture: Foundations for Sustainable Infrastructure John Doyle John G Braun Professor Control and Dynamical Systems, EE, BE Caltech

“Universal laws and architectures?” Universal “conservation laws” (constraints) Universal architectures (constraints that deconstrain) Mention recent papers* Focus on broader context not in papers Lots of case studies for motivation *try to get you to read them? Systems Fundamentals! A rant

Lots from cell biology –glycolytic oscillations for hard limits –bacterial layering for architecture Networking and “clean slate” architectures –wireless end systems –info or content centric application layer –integrate routing, control, scheduling, coding, caching –control of cyber-physical –PC, OS, VLSI, antennas, etc (IT components) Neuroscience Smartgrid, cyber-phys Wildfire ecology Medical physiology Earthquakes Lots of aerospace Physics: turbulence, stat mech (QM?) “Toy”: Lego, clothing, buildings, … Fundamentals! my case studies

Lots from cell biology –glycolytic oscillations for hard limits –bacterial layering for architecture Networking and “clean slate” architectures –wireless end systems –info or content centric application layer –integrate routing, control, scheduling, coding, caching –control of cyber-physical –PC, OS, VLSI, antennas, etc (IT components) Neuroscience Smartgrid, cyber-phys Wildfire ecology Medical physiology Earthquakes Lots of aerospace Physics: turbulence, stat mech (QM?) “Toy”: Lego, clothing, buildings, … Fundamentals! my case studies

wasteful fragile efficient robust Want to understand the space of systems/architectures Want robust and efficient systems and architectures Some old stuff to warm up

accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective efficient evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable robust safety scalable seamless self-sustainable serviceable supportable securable simplicity stable standards compliant survivable sustainable tailorable testable timely traceable ubiquitous understandable upgradable usable Requirements on systems and architectures

accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective efficient evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable robust safety scalable seamless self-sustainable serviceable supportable securable simple stable standards compliant survivable sustainable tailorable testable timely traceable ubiquitous understandable upgradable usable Simplified, minimal requirements

accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable safety scalable seamless self-sustainable serviceable supportable securable stable standards compliant survivable tailorable testable timely traceable ubiquitous understandable upgradable usable Requirements on systems and architectures efficient robust simple sustainable

Requirements on systems and architectures efficient robust sustainable simple

Requirements on systems and architectures efficient robust sustainable simple

Requirements on systems and architectures efficient robust simple sustainable fragile wasteful complex

Requirements on systems and architectures efficient robust simple sustainable fragile wasteful complex

wasteful fragile efficient robust Want to understand the space of systems/architectures Hard limits on robust efficiency? Case studies? Strategies? Architectures? Want robust and efficient systems and architectures

Future evolution of the “smart” grid? efficient robust fragile wasteful Now Future?

wasteful fragile robust efficient At best we get one Current Technology?

wasteful fragile robust efficient Often neither  ??? 

Bad theory?  ???  ? ? Bad architectures? wasteful fragile gap? robust efficient

Control, ORComms Compute Physics Shannon Bode Turing Godel Einstein Heisenberg Carnot Boltzmann Theory? Deep, but fragmented, incoherent, incomplete Nash Von Neumann Kalman Pontryagin

Compute Turing ( ) Turing 100 th birthday in 2012 Turing −machine (math, CS) −test (AI, neuroscience) −pattern (biology) Arguably greatest* −all time math/engineering combination −WW2 hero −“invented” software *Also world-class runner.

Key papers/results Theory (1936): Turing machine (TM), computability, (un)decidability, universal machine (UTM) Practical design (early 1940s): code-breaking, including the design of code-breaking machines Practical design (late 1940s): general purpose digital computers and software, layered architecture Theory (1950): Turing test for machine intelligence Theory (1952): Reaction diffusion model of morphogenesis, plus practical use of digital computers to simulate biochemical reactions

ControlComms Compute Physics Shannon Bode Turing Godel Einstein Heisenberg Carnot Boltzmann wasteful? fragile? slow? ? Each theory  one dimension Tradeoffs across dimensions Assume architectures a priori Progress is encouraging, but… Stovepipes are an obstacle…

Cyberphysical theories Cyber (digital) Turing computation (time) Shannon compression (space) Content centric nets (time, space, location) Physical (analog) Bode (latency) Shannon (channels) Networked control (AndyL) Redo StatMech and efficiency Layering as optimization? Lots of challenges not yet addressed (e.g. Smartgrid, biology, neuro,..)

Turing’s 3 step research: 0. Virtual (TM) machines 1.hard limits, (un)decidability using standard model (TM) 2.Universal architecture achieving hard limits (UTM) 3.Practical implementation in digital electronics (biology?) Essentials: 0.Model 1.Universal laws 2.Universal architecture 3.Practical implementation Software Hardware Digital Analog Turing as “new” starting point?

…being digital should be of greater interest than that of being electronic. That it is electronic is certainly important because these machines owe their high speed to this… But this is virtually all that there is to be said on that subject. That the machine is digital however has more subtle significance. … One can therefore work to any desired degree of accuracy. This accuracy is not obtained by more careful machining of parts, control of temperature variations, and such means, but by a slight increase in the amount of equipment in the machine 1947 Lecture to LMS

Software Hardware Digital Analog … quite small errors in the initial conditions can have an overwhelming effect at a later time. The displacement of a single electron by a billionth of a centimetre at one moment might make the difference between a man being killed by an avalanche a year later, or escaping. It is an essential property of the mechanical systems which we have called 'discrete state machines' that this phenomenon does not occur. Even when we consider the actual physical machines instead of the idealised machines, reasonably accurate knowledge of the state at one moment yields reasonably accurate knowledge any number of steps later. 1950, Computing Machinery and Intelligence, Mind

The 'skin of an onion' analogy is also helpful. In considering the functions of the mind or the brain we find certain operations which we can explain in purely mechanical terms. This we say does not correspond to the real mind: it is a sort of skin which we must strip off if we are to find the real mind. But then in what remains we find a further skin to be stripped off, and so on. Proceeding in this way do we ever come to the 'real' mind, or do we eventually come to the skin which has nothing in it? In the latter case the whole mind is mechanical. 1950, Computing Machinery and Intelligence, Mind

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Cyberphysical Starting point Software Hardware Digital Analog

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Cyberphysical

The “plant” Physical net -physical Current power grid gets plug and play stability and robustness from passive, lossy networks and centralized generation. All of this will change.

Software Hardware Digital Analog Active Lumped Distribute Computers Software Hardware Digital Analog Active Lumped Distribute Computers Comms net Virtual net Cyber- Internet gets p&p from protocols. Packets are easy compared with physical flows.

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Cyberphysical Can we design protocol-based control of CPS systems?

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Cyberphysical Starting point Software Hardware Digital Analog

Cyber focus Starting point Start from here with Turing’s 3 step research: 1.(universal) hard limits, (un)decidability using standard model (TM) 2.Universal architecture achieving hard limits (UTM) 3.Practical implementation in digital electronics ( and biology?) Software Hardware Digital Analog

Abstractions Want to specialize 3 notions of abstraction: Layer Virtual Universal (laws, architectures, implementation) (see Rant, not sure about best terminology)

Cyber focus Software Hardware Digital Analog Active Lumped Distribute Comms net Virtual net Steps (mimicking Turing) Turing (time, delay only) Space, storage, compression ( Shannon) Layering as utility max (time, space) Tradeoffs Time (latency/delay in comp and comm) Space (storage and location)

Active Lumped Distribute The “plant” Controller/ actuators/ sensors/ amplifiers Active Lumped Distribute The “plant” Comms net Physical net Virtual net -physical focus Controller/ actuators/ sensors/ amplifiers

Active Lumped The “plant” Controller/ actuators/ sensors/ amplifiers -physical focus Start from Bode: 1.Hard limits (noise, delay) 2.Architecture (filter+control) 3.Practical implementation issues

Active Lumped Distribute Controller/ actuators/ sensors/ amplifiers Active Lumped Distribute Comms net Virtual net -physical focus Controller/ actuators/ sensors/ amplifiers One direction (Bode-Shannon) Shannon channels, theory Bode-Shannon, control with channels

Active Lumped Distribute The “plant” Active Lumped Distribute The “plant” Comms net Physical net Virtual net One direction Shannon channels, theory Bode-Shannon Another (Andy L) Delays networks of delays Tradeoffs Delays (sense/act, plant, comms, comp) Noise (sense/act, channels, disturbances) Saturation (sense/act, comms bw)

Basic confusions in layered systems Easy: What is software (if in reality/physics there is only hardware) Related physics problems: Where does dissipation come from (if reality/physics is actually lossless)… and noise, amplification, etc Are uncertainty principles (e.g.  p  x>c.) necessarily only quantum mechanical? (no) What are truly far from equilibrium systems and do they violate the 2 nd law? (related to amplification and control) etc An actual hard one Where does excess drag come from in turbulent flows?

3 distinct (but entwined) kinds of layering Software Hardware Apps Libs, IPC kernel Digital Analog Active Lumped Distribute are very different Digital Analog Active Lumped Distribute Analog Active Lumped Distribute The world Layers here from layers here Actuators/ sensors/ amplifiers Computers Computer Actuator/sensor Physical plant one kind of layering

3 distinct (but entwined) kinds of layering Software Hardware Apps Libs, IPC kernel Digital Analog Active Lumped Distribute are very different Digital Analog Active Lumped Distribute Analog Active Lumped Distribute The world Layers here from layers here Actuators/ sensors/ amplifiers Computers Computer Actuator/sensor Physical plant one kind of layering

Apps Libs, IPC kernel Software is layered

Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute Analog Active Lumped Distribute The world layers Hardware is “layered”

3 distinct (but entwined) kinds of layering Software Hardware Apps Libs, IPC kernel Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute Analog Active Lumped Distribute The world Layers here from layers here are very different Actuators/ sensors/ amplifiers Computers

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Cyberphysical

Just as with the Internet, there is a complex layering of real and virtual networks with their own dynamics, semantics, etc Comms net Physical net Virtual net

Software Hardware Digital Analog Active Lumped Distribute Computers Software Hardware Digital Analog Active Lumped Distribute Computers Comms net Virtual net Cyber focus Comms net Virtual net Comms net Virtual net

Software Hardware Apps Libs, IPC kernel Digital Analog Active Lumped Distribute are very different 3 distinct (but entwined) kinds of layering Digital Analog Active Lumped Distribute Analog Active Lumped Distribute The world Layers here from layers here Cyberphysical systems must have this kind of layering

Antenna Software Hardware Apps Libs, IPC kernel Digital Analog Software Hardware Apps Libs, IPC kernel Digital Analog Active Lumped Distribute Antenna Active Lumped Distribute Wireless Layers here from layers here are very different from layers here 3 distinct (but entwined) kinds of layering

Maybe start from here with Turing’s 3 step research: 1.hard limits, (un)decidability using standard model (TM) 2.Universal architecture achieving hard limits (UTM) 3.Practical implementation in digital electronics (biology?) Essentials: 0.Model 1.Universal laws 2.Universal architecture 3.Practical implementation Software Hardware Digital Analog Turing as “new” starting point?

Building on Turing What I basically want to explain is what engineers and mathematicians take too for granted about Turing’s ideas, and scientists completely miss, with particular focus on the relevance these ideas have for the future of systems biology and CPS. Starting from Turing go in many directions…

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Cyberphysical

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Sense

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Decide

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Act

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers SenseDecideAct This “roundtrip” delay is huge factor in control robustness and performance

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net Comms

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Computers Comms net Physical net Virtual net DecideActCommsSense

DecideActCommsSense Communication delay c Act/Plant/Sense Delay a Lamperski

DecideActSense Act/Plant/Sense Delay a

DecideComms Communication delay c

Lamperski Act/Plant/Sense Delay a

Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Software Hardware Digital Analog Active Lumped Distribute Digital Analog Active Lumped Distribute The “plant” Actuators/ sensors/ amplifiers Comms net Physical net Virtual net

The “plant” Comm delay c Want c and d small relative to a Local control delay d Act/Plant/Sense “Global “ Delay a Plant

ControlComms Compute Physics Shannon Bode Turing Godel Einstein Heisenberg Carnot Boltzmann wasteful? fragile? slow? ? Each theory  one dimension Tradeoffs across dimensions Assume architectures a priori Progress is encouraging, but… Stovepipes are an obstacle…

Case studies wasteful fragile Sharpen hard bounds Hard limit laws and architectures? efficient robust

Csete and Doyle

Chandra, Buzi, and Doyle UG biochem, math, control theory Most important paper so far.

K Nielsen, PG Sorensen, F Hynne, H-G Busse. Sustained oscillations in glycolysis: an experimental and theoretical study of chaotic and complex periodic behavior and of quenching of simple oscillations. Biophys Chem 72:49-62 (1998). Experiments CSTR, yeast extracts

Figure S4. Simulation of two state model (S7.1) qualitatively recapitulates experimental observation from CSTR studies [5] and [12]. As the flow of material in/out of the system is increased, the system enters a limit cycle and then stabilizes again. For this simulation, we take q=a=Vm=1, k=0.2, g=1, u=0.01, h=2.5. “Standard” Simulation

Figure S4. Simulation of two state model (S7.1) qualitatively recapitulates experimental observation from CSTR studies [5] and [12]. As the flow of material in/out of the system is increased, the system enters a limit cycle and then stabilizes again. For this simulation, we take q=a=Vm=1, k=0.2, g=1, u=0.01, h=2.5. SimulationExperiments Why?

Levels of explanation: 1.Possible 2.Plausible 3.Actual 4.Mechanistic 5.Necessary Science Engineering Medicine

Glycolytic “circuit” and oscillations Most studied, persistent mystery in cell dynamics End of an old story (why oscillations) – side effect of hard robustness/efficiency tradeoffs – no purpose per se – just needed a theorem Beginning of a new one – robustness/efficiency tradeoffs – complexity and architecture – need more theorems and applications Fundamentals!

simple enzyme Fragility Enzyme amount complex enzyme Theorem! z and p functions of enzyme complexity and amount Savageaumics

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Inside every cell almost

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Energy Protein biosyn

Minimal model Catabolism Precursors ATP Energy ATP Rest of cell energy ATP Yeast anaerobic glycolysis

ATP Autocatalytic feedback Rest of cell energy x ATP Reaction 2 (“PK”) Reaction 1 (“PFK”) intermediate metabolite Minimal model Catabolism Precursors ATP Energy Yeast anaerobic glycolysis

Robust = Maintain energy (ATP concentration) despite demand fluctuation ATP Rest of cell energy x ATP h g control Reaction 2 (“PK”) Reaction 1 (“PFK”) disturbance control feedback Tight control creates “weak linkage” between power supply and demand

Fragile Robust ATP Rest of cell energy ATP disturbance Tight control creates “weak linkage” between power supply and demand Robust = Maintain energy (ATP concentration) despite demand fluctuation

ATP Rest of cell Reaction 2 (“PK”) Reaction 1 (“PFK”) Protein biosyn energy enzymes Efficient = low metabolic overhead  low enzyme amount enzymes catalyze reactions

Fragile Robust WastefulEfficient Robust = Maintain ATP Efficient = low enzyme amount  ATP ? ?

simple enzyme Fragility Enzyme amount complex enzyme Theorem! z and p functions of enzyme complexity and amount Savageaumics

Architecture “Conservation laws” Good architectures allow for effective tradeoffs wasteful fragile Alternative systems with shared architecture

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Inside every cell almost

simple tech complex tech How general is this picture? wasteful fragile efficient robust Implications for human evolution? Cognition? Technology? Basic sciences?

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Inside every cell almost

Catabolism AA Ribosome transl. Proteins Precursors Nucl. AA ATP Inside every cell Ribosomes make ribosomes Translation: Amino acids polymerized into proteins almost

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Building Blocks Translation Transcription DNA Replication

cheap fast slow (  fragile) expensive Tradeoffs? metabolic overhead control response

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP cheap fast slow expensive Tradeoffs drawn awkwardly

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gen e ATP cheap fast slow expensive

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP cheap fast slow expensive Tradeoffs redrawn

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP cheap fast slow expensive Tradeoffs redrawn

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Layered view

AA RNA transl. Proteins xRNA transc. Precursors Nucl. AA DNA Repl. Gene ATP Enzymes Shared protocols NAD Universal core constraints “virtual machines”

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Autocatalytic feedbacks <10% of most bacterial genomes 100s of genes

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control feedbacks? Control

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control feedbacks?

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control Complexity of control is huge and poorly studied.

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control It is control that is fast/slow and expensive/cheap cheap fast slow expensive

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Layered view

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control >90% of most bacterial genomes <10% of most bacterial genomes ~300 genes, ~minimal genome, requires idealized environment

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control Environment Action >90% of most bacterial genomes What about “natural” environments?

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control Environment Action >90% of most bacterial genomes

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Control >90% of most bacterial genomes Environment Action

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Complex machines − Polymerization − Complex assembly General enzymes Regulated recruitment Slow, efficient control Quantized, digital Complex machines − Polymerization − Complex assembly General enzymes Regulated recruitment Slow, efficient control Quantized, digital Building blocks − Scavenge − Recycle − Biosynthesis Special enzymes Allostery, Fast Expensive control Analog Building blocks − Scavenge − Recycle − Biosynthesis Special enzymes Allostery, Fast Expensive control Analog

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP cheap fastslow expensive

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP DNA replication − Highly controlled − Facilitated variation − Accelerates evolution DNA modification (e.g. methylation) Complex RNA control DNA replication − Highly controlled − Facilitated variation − Accelerates evolution DNA modification (e.g. methylation) Complex RNA control Homeostasis − pH − Osmolarity − etc Cell envelope Movement, attachment, etc Homeostasis − pH − Osmolarity − etc Cell envelope Movement, attachment, etc What we’ve neglected here Ecosystems Biofilms Extremophiles Pathogens Symbiosis … Ecosystems Biofilms Extremophiles Pathogens Symbiosis …

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gen e ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Inside every cell

Building Blocks Lower layer autocatalysis Macromolecules making … Three lower layers? Yes: Translation Transcription Replication AA Ribosome RNA RNAp transl. Proteins xRNA transc. Enzymes DNA DNAp Repl. Gen e

Building Blocks AA Ribosome transl. Proteins xRNA Enzymes Translation Transcription Replication

Building Blocks RNA RNAp transl. Proteins xRNA transc. Enzymes Gen e Translation Transcription Replication

Building Blocks transl. Proteins xRNA transc. Enzymes DNA DNAp Repl. Gen e Translation Transcription Replication

transl. Proteins mRNA transc. Gen e Pathway views Catabolism Precursors Nucl. AA ATP Repl.

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP cheap fast slow expensive Tradeoffs redrawn

simple tech complex tech How general is this picture? wasteful fragile efficient robust Implications for human evolution? Cognition? Technology? Basic sciences?

TCP IP Physical MAC Switch MAC Pt to Pt Diverse applications Layered architectures

Proceedings of the IEEE, Jan 2007 Chang, Low, Calderbank, Doyle OR optimization What’s next? Fundamentals! A rant

TCP IP Physical Diverse applications Diverse Too clever?

TCP IP Deconstrained (Hardware) Deconstrained (Applications) Layered architectures Constrained Networks “constraints that deconstrain” (Gerhart and Kirschner)

TCP/ IP Deconstrained (Hardware) Deconstrained (Applications) Constrained Facilitated wild evolution Created whole new ecosystem completely opposite Why? OS better starting point than phone/comms systems Extreme robustness confers surprising evolvability Creative engineers Rode hardware evolution Networked OS Architecture

OS Deconstrained (Hardware) Deconstrained (Applications) Layered architectures Constrained Control, share, virtualize, and manage resources Processing Memory I/O Few global variables Don’t cross layers Essentials

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Shared protocols Deconstrained (diverse) Environments Deconstrained (diverse) Genomes Bacterial biosphere Architecture = Constraints that Deconstrain Layered architectures

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Crosslayer autocatalysis Macro-layers Inside every cell almost

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Core conserved constraints facilitate tradeoffs Deconstrained phenotype Deconstrained genome What makes the bacterial biosphere so adaptable? Active control of the genome (facilitated variation) Environment Action Layered architecture

OS Deconstrained (Hardware) Deconstrained (Applications) Layered architectures Constrained Control, share, virtualize, and manage resources Processing Memory I/O Few global variables Don’t cross layers Direct access to physical memory?

Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks Shared protocols Deconstrained (diverse) Environments Deconstrained (diverse) Genomes Bacterial biosphere Architecture = Constraints that Deconstrain Few global variables Don’t cross layers

Problems with leaky layering Modularity benefits are lost Global variables? Poor portability of applications Insecurity of physical address space Fragile to application crashes No scalability of virtual/real addressing Limits optimization/control by duality?

Fragilities of layering/virtualization “Universal” fragilities that must be avoided Hijacking, parasitism, predation –Universals are vulnerable –Universals are valuable Cryptic, hidden –breakdowns/failures –unintended consequences Hyper-evolvable but with frozen core

TCP/ IP Deconstrained (Hardware) Deconstrained (Applications) Layered architectures Constrained Control, share, virtualize, and manage resources I/O Comms Latency? Storage? Processing? Few global variables? Don’t cross layers?

App IPC Global and direct access to physical address! DNS IP addresses interfaces (not nodes) caltech.edu?

App IPC Global and direct access to physical address! Robust? Secure Scalable Verifiable Evolvable Maintainable Designable … DNS IP addresses interfaces (not nodes)

Naming and addressing need to be resolved within layer translated between layers not exposed outside of layer Related “issues” VPNs NATS Firewalls Multihoming Mobility Routing table size Overlays …

TCP IP Physical Diverse Until late 1980s, no congestion control, which led to “congestion collapse”

Original design challenge? TCP/ IP Deconstrained (Hardware) Deconstrained (Applications) Constrained Expensive mainframes Trusted end systems Homogeneous Sender centric Unreliable comms Facilitated wild evolution Created whole new ecosystem completely opposite Networked OS

? Deconstrained (Hardware) Deconstrained (Applications) Next layered architectures Constrained Control, share, virtualize, and manage resources Comms Memory, storage Latency Processing Cyber-physical Few global variables Don’t cross layers

Every layer has different diverse graphs. Architecture is least graph topology. Architecture facilitates arbitrary graphs. Persistent errors and confusion (“network science”) Physical IP TCP Application

Evolution and architecture Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky (see also de Chardin) Nothing in evolution makes sense except in the light of biology ?????

How general is this picture? Very! Constraints! i.e. hard limits and architecture simple tech complex tech wasteful fragile efficient robust Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene ATP Enzymes Building Blocks

plasmids virus HGT  huge new insights! So what’s missing? Explaining clearly the fundamental role that layered architectures play in making facilitated variation possible. Now have extensive, and hopefully accessible case studies: 1.Bacterial biosphere and HGT 2.Evolution of human brain (EvoDevo still too hard)

plasmids virus HGT Note: The pieces underlying these new views are widely accepted, but the consequences are emphatically not! Why so much resistance? A big reason: Big (wrong) idea: All complexity is emergent from random ensembles with minimal tuning Sketch how this plays out

Standard (old) theory: The modern synthesis = natural selection + genetic drift + mutation + gene flow What’s missing here? Almost everything

Evolution fact and theory The fact of evolution is well-known – Organisms and their genes change over time – Seen in labs, hospitals, barnyards, fossil records, etc Darwin gave plausible explanation – Variation and selection are both observable – Molecular mechanisms are not known Modern synthesis gave molecular mechanism Theory has evolved and deepened What is “standard” modern synthesis theory now?

Gene alleles Selection Standard theory: natural selection + genetic drift + mutation + gene flow Shapiro explains well what this is and why it’s incomplete Greatly abridged cartoon here

Standard theory: natural selection + genetic drift + mutation + gene flow Assumptions: Genome inherited from parent Mutations due to replication errors At constant rate, rare, small, localized Limited to small, usually deleterious effect Selection as “creative” mechanism Gene alleles Selection Examples abound Math straightforward and obvious, like stat phys No architecture or anything that smells like engineering No new principles beyond physics and chemistry

Assumptions strengthened: Genome inherited only from parent Mutations due only to replication errors Only at constant rate, rare, small, localized Limited only to small, usually deleterious effect Selection only “creative” mechanism Mutations can’t be “targeted” within the genome Can’t coordinate DNA change w/ useful adaptive needs Viruses can’t induce DNA change giving heritable resistance Big (wrong) idea: All biological complexity is emergent via random mutations with small and minimal tuning via natural selection The extreme version

In retrospect, why is the conventional theory so popular ? Standard assumptions: Inherit only from parent Mutations = replication errors Constant rate, rare, small, localized Usually deleterious Selection = “creative” mechanism Mutations can’t be “targeted” or coordinated Viruses can’t induce heritable resistance Gene alleles Selection Simple, fits theory of “random plus minimal tuning” Examples abound Math straightforward and obvious, like stat phys No architecture or anything that smells like engineering No new principles beyond physics and chemistry No real phenotype, just abstract “fitness landscapes” Focuses on genomes which can now be measured Strongly rejects creationism and any sort of “design” But still allows for the mystical (e.g. “order for free”)

Examples abound that fit the theory Standard assumptions: Inherit only from parent Mutations = replication errors Constant rate, rare, small, localized Usually deleterious Selection = “creative” mechanism Mutations can’t be “targeted” or coordinated Viruses can’t induce heritable resistance Gene alleles Selection Cancer: Cells evolve new robustness via largely random mutations Hijack robust, evolvable architectures Tumors also hijack system resources Eventually kill host and themselves Example of evolution as grotesquely myopic Bacterial clones (circa 1940): Exponential growth from one cell in ideal lab conditions Accumulates random neutral mutations Generating (“scale-free”) phylogenetic tree of mutants

Standard theory: natural selection + genetic drift + mutation + gene flow Assumptions: Genome inherited only from parent Mutations due to replication errors At constant rate, rare, small, localized Limited to small, usually deleterious effect Selection only “creative” mechanism Mutations can’t be “targeted” within the genome Can’t coordinate DNA change w/ useful adaptive needs Viruses can’t induce DNA change giving heritable resistance Gene alleles Selection Gerhart and Kirschner, Shapiro, Caporale, Lane, etc etc show  facts proving this is wildly incomplete. (Even e.g. Koonin agrees with this though differing in details) Organisms wildly violate all of them. But the theory needed is now vastly more complex.

Gerhart and Kirschner Facilitated variation Architecture = Constraints that deconstrain Weak linkage Exploratory mechanisms Compartmentalization (EvoDevo) Unpopular within biology, see Erwin’s review in Cell 2005

EvoDevo= Evolution + development Hottest part of evolutionary theory Most complicated relationship between geno- and pheno-type Many unresolved issues Ignore for now Focus primarily on microbes and large evolutionary transitions

Many mechanisms for “horizontal” gene transfer Many mechanisms to create large, functional mutations At highly variable rate, can be huge, global Selection alone is a very limited filtering mechanism Mutations can be “targeted” within the genomes Can coordinate DNA change w/ useful adaptive needs Viruses can induce DNA change giving heritable resistance Still myopic about future, still produces the grotesque

Now almost everyone agrees with facts of an “extended” synthesis: Many mechanisms for “horizontal” gene transfer Many mechanisms to create large, functional (or neutral) mutations At highly variable rate, can be huge, global Selection alone is a very limited filtering mechanism Mutations can be “targeted” within the genomes Can coordinate DNA change w/ useful adaptive needs Viruses can induce DNA change giving heritable resistance Modern synthesis assumptions: Genome inherited only from parent Mutations due to replication errors At constant rate, rare, small, localized Limited to small, usually deleterious effect Selection only “creative” mechanism Mutations can’t be “targeted” within the genome Can’t coordinate DNA change w/ useful adaptive needs Viruses can’t induce DNA change giving heritable resistance So what are the remaining issues? Lots!

Now almost everyone agrees with facts of an “extended” synthesis: Many mechanisms for “horizontal” gene transfer Many mechanisms to create large, functional (or neutral) mutations At highly variable rate, can be huge, global Selection alone is a very limited filtering mechanism Mutations can be “targeted” within the genomes Can coordinate DNA change w/ useful adaptive needs Viruses can induce DNA change giving heritable resistance Conjecture: Much current controversy caused by ignoring layered arch. from genome to phenome confusion between large and thin in layers Oversimplifying: Two main “camps” 1.Minimal tweak modern syn, minimal architecture 2.Maximal architecture So what are the remaining issues? Lots!

The minimal architecture camp Definite, solid progress Reconciles “stat phys” view with many “new facts” large but closeable gap to maximal view

The maximal architecture camp Challenge: distill the essence of their insights Architecture = constraints that deconstrain Evolving evolvability “bacterial Internet” (Caporale) Try to close gap from max to min camp

natural selection + genetic drift + mutation + gene flow + facilitated variation HGT = horizontal gene transfer plasmid s virus HGT large functional changes in genomes

Genes Stress Selection natural selection + genetic drift + mutation + gene flow + facilitated variation Genome can have large changes

Geno- type Stress acts on phenome Selection acts on phenome natural selection + genetic drift + mutation + gene flow + facilitated variation Small gene change can have large but functional phenotype change Pheno- type Architecture

Selection acts on phenome natural selection + genetic drift + mutation + gene flow + facilitated variation Small gene change can have large but functional phenotype change Only possible because of shared, layered, network architecture Geno- type Stress acts on phenome Pheno- type Architecture

Highlighted differences Old 1.Randomness in genetic change New 1.Randomness in environment – HGT – Stress on organism

"...variations which seem to us in our ignorance to arise spontaneously. It appears that I formerly underrated the frequency and value of these latter forms of variation, as leading to permanent modifications of structure independently of natural selection.” Darwin, Origin of Species, 6 th edition, Chapter XV, p Darwin on facilitated variation

plasmids virus HGT Some facts about bacteria: ~100 core conserved genes (that define architecture) by lineage descent only Most other (  ) genes acquired (sometime) by HGT “minimal” genome includes core conserved genes plus ~200 more what gives? “Constraints that deconstrain” = architecture Bacterial genomes/phylogenies are great case studies Only the facts and the components are (so far) explained Needs layered architecture theory to hang the facts on Initial paper can be written now

plasmids virus HGT More facts about bacteria: In humans, # bacterial cells  10x # human cells In humans, # bacterial genes >100x # human genes In ocean, >1e30 bacterial cells, >1e31 viruses In ocean, viruses kill half of bacterial cells every day Eukaryotes cells created by fusion of prokaryote cells Layered architecture crucial enabler New discoveries of intermediates?

plasmids virus HGT What’s missing? Explaining clearly the fundamental role that layered architectures play in making facilitated variation possible. Now have extensive, and hopefully accessible, case studies: 1.Bacterial biosphere and HGT 2.Evolution of human brain (EvoDevo still too hard)

Unfortunately, not intelligent design Ouch. Outcomes can be grotesque, maladaptive. Therefore, dangerous to rely on evolution alone, no matter how facilitated

Evolution theory 1.0 assumptions: Inherit only from parent Mutations = replication errors Constant rate, rare, small, localized Usually deleterious Selection = “creative” mechanism Mutations can’t be “targeted” or coordinated Viruses can’t induce heritable resistance Cancer: Cells evolve new robustness via largely random mutations Hijack robust, evolvable architectures Tumors also hijack system resources Eventually kill host and themselves Example of evolution as grotesquely myopic Evolution facts 1.0 (examples) Bacterial clones (circa 1940): Exponential growth from one cell in ideal lab conditions Accumulates random neutral mutations Generating (“scale-free”) phylogenetic tree of mutants Modern Synthesis: Great start iff taken as only a start

Evolution theory 2.0 assumptions? Horizontal transfer +++ Mutation highly variable but not random Fast and slow, large and small, local and global Facilitated variation is main creative force Selection is weak filter on unfit Evolution facts 2.0? Tons of facts that violate theory 1.0, now in books Evolution theory 2.0? Constraints that deconstrain is a start Need layered architecture story that hasn’t been told

simple tech complex tech How general is this picture? wasteful fragile efficient robust Implications for human evolution? Cognition? Technology? Basic sciences?

simple enzyme Fragility Enzyme amount complex enzyme Theorem! z and p functions of enzyme complexity and amount Savageaumics

Fragility hard limits simple Overhead, waste complex General Rigorous First principle Domain specific Ad hoc Phenomenological Plugging in domain details ?

ControlComms Physics Wiener Bode Kalman Heisenberg Carnot Boltzmann robust control Fundamental multiscale physics Foundations, origins of – noise – dissipation – amplification – catalysis General Rigorous First principle ? Shannon

Stat physics Complex networks Physics Heisenberg Carnot Boltzmann ControlComms Compute “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,… Wildly “successful”

D. Alderson, NPS177 Popular but wrong

doesn’t work Stat physics Complex networks Alderson & Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,…

Stat physics Complex networks Physics Heisenberg Carnot Boltzmann Control Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation- Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011

Stat physics, Complex networks Physics Heisenberg Carnot Boltzmann fluids, QM “orthophysics” From prediction to mechanism to control Fundamentals! Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation- Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 “The last 70 years of the 20 th century will be viewed as the dark ages of theoretical physics.” (Carver Mead)

J. Fluid Mech (2010) Transition to Turbulence Flow Streamlined Laminar Flow Turbulent Flow Increasing Drag, Fuel/Energy Use and Cost Turbulence and drag?

Physics of Fluids (2011) Dennice Gayme, Beverley McKeon, Bassam Bamieh, Antonis Papachristodoulou, John Doyle

Physics of Fluids (2011) z x y z x y Flow upflow high-speed region downflow low speed streak Blunted turbulent velocity profile Laminar Turbulent 3D coupling Coherent structures and turbulent drag

wasteful fragile Laminar Turbulent efficient robust Laminar Turbulent ? Control? Fundamentals!

J. Fluid Mech (2010) Transition to Turbulence Flow Streamlined Laminar Flow Turbulent Flow Increasing Drag, Fuel/Energy Use and Cost Turbulence and drag?

Physics of Fluids (2011) Dennice Gayme, Beverley McKeon, Bassam Bamieh, Antonis Papachristodoulou, John Doyle

Physics of Fluids (2011) z x y z x y Flow upflow high-speed region downflow low speed streak Blunted turbulent velocity profile Laminar Turbulent 3D coupling Coherent structures and turbulent drag

wasteful fragile Laminar Turbulent efficient robust Laminar Turbulent ? Control? Fundamentals!

simple tech complex tech How general is this picture? wasteful fragile efficient robust Implications for human evolution? Cognition? Technology? Basic sciences?

This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Doyle and Csete, Proc Nat Acad Sci USA, online JULY Very accessible No math

Evolution and architecture Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky (see also de Chardin) Nothing in evolution makes sense except in the light of biology ?????

natural selection + genetic drift + mutation + gene flow ++ architecture

Gerhart and Kirschner Facilitated variation Architecture = Constraints that deconstrain Weak linkage Exploratory mechanisms Compartmentalization

Unfortunately, not intelligent design Ouch.

weak fragile slow strong robust fast Human evolution Apes How is this progress? hands feet skeleton muscle skin gut long helpless childhood All very different.

inefficient wasteful weak fragile efficient (slow) strong robust hands feet skeleton muscle skin gut This much seems pretty consistent among experts regarding circa 1.5-2Mya Homo Erectus? Roughly modern So how did H. Erectus survive and expand globally? Very fragile

inefficient wasteful weak fragile efficient (slow) strong robust (fast) Apes speed & strength endurance Biology

inefficient wasteful weak fragile (slow) efficient (slow) strong robust (fast) Biology Human evolution Apes hands feet skeleton muscle skin gut

inefficient wasteful weak fragile efficient (slow) strong robust Biology Hard tradeoffs? Apes Architecture? Evolvable?

weak fragile efficient (slow) strong robust + sticks stones fire teams From weak prey to invincible predator? Speculation? There is only evidence for crude stone tools. But sticks, fire, teams might not leave a record? endurance

weak fragile efficient (slow) strong robust + sticks stones fire teams From weak prey to invincible predator Before much brain expansion? Speculation? With only evidence for crude stone tools. But sticks and fire might not leave a record? Plausible but speculation?

Today 2Mya Cranial capacity Gap? Before much brain expansion?

weak fragile efficient (slow) strong robust hands feet skeleton muscle skin gut + sticks stones fire From weak prey to invincible predator Before much brain expansion? Key point: Our physiology, technology, and brains have co- evolved Huge implications. Probably true no matter what

Today 2Mya Cranial capacity

weak fragile efficient (slow) strong robust hands feet skeleton muscle skin gut + sticks stones fire From weak prey to invincible predator Before much brain expansion? Key point needing more discussion: The evolutionary challenge of big brains is homeostasis, not basal metabolic load. Huge implications. Fundamentals!

inefficient wasteful weak fragile efficient (slow) strong robust Biology + sticks stones fire +Technology Architecture?

weak fragile efficient (slow) strong robust hands feet skeleton muscle skin gut + sticks stones fire From weak prey to invincible predator Before much brain expansion?

inefficient wasteful weak fragile efficient (slow) strong robust Biology + sticks stones fire +Technology Architecture?

Biology sticks stones fire +Technology Human complexity? wasteful fragile efficient robust Consequences of our evolutionary history?

RobustFragile Human complexity Metabolism Regeneration & repair Healing wound /infect  Obesity, diabetes  Cancer  AutoImmune/Inflame Start with physiology Lots of triage

Robust Benefits Metabolism Regeneration & repair Healing wound /infect Efficient Mobility Survive uncertain food supply Recover from moderate trauma and infection

RobustFragile Mechanism? Metabolism Regeneration & repair Healing wound /infect  Fat accumulation  Insulin resistance  Proliferation  Inflammation  Obesity, diabetes  Cancer  AutoImmune/Inflame  Fat accumulation  Insulin resistance  Proliferation  Inflammation

RobustFragile What’s the difference? Metabolism Regeneration & repair Healing wound /infect  Obesity, diabetes  Cancer  AutoImmune/Inflame  Fat accumulation  Insulin resistance  Proliferation  Inflammation Controlled Dynamic Uncontrolled Chronic

Controlled Dynamic Low mean High variability  Fat accumulation  Insulin resistance  Proliferation  Inflammation

Controlled Dynamic Uncontrolled Chronic Low mean High variability High mean Low variability  Fat accumulation  Insulin resistance  Proliferation  Inflammation Death

RobustFragile Restoring robustness? Metabolism Regeneration & repair Healing wound /infect  Obesity, diabetes  Cancer  AutoImmune/Inflame  Fat accumulation  Insulin resistance  Proliferation  Inflammation Controlled Dynamic Uncontrolled Chronic Low mean High variability High mean Low variability  Fat accumulation  Insulin resistance  Proliferation  Inflammation

inefficient wasteful weak fragile efficient (slow) strong robust Biology +Technology ++Technology

RobustYet Fragile Human complexity Metabolism Regeneration & repair Immune/inflammation Microbe symbionts Neuro-endocrine  Complex societies  Advanced technologies  Risk “management”  Obesity, diabetes  Cancer  AutoImmune/Inflame  Parasites, infection  Addiction, psychosis,…  Epidemics, war,…  Disasters, global &!%$#  Obfuscate, amplify,… Accident or necessity?

RobustFragile Metabolism Regeneration & repair Healing wound /infect  Obesity, diabetes  Cancer  AutoImmune/Inflame  Fat accumulation  Insulin resistance  Proliferation  Inflammation Fragility  Hijacking, side effects, unintended… Of mechanisms evolved for robustness Complexity  control, robust/fragile tradeoffs Math: robust/fragile constraints (“conservation laws”) Accident or necessity? Both Fundamentals!

wasteful fragile efficient robust Hard tradeoffs? Architecture? Constraints (that deconstrain)

Meta-layers Physiology Organs Prediction Goals Actions errors Actions Cortex Fast, Limited scope Slow, Broad scope Which blue line is longer ? “Seeing is dreaming?” “Seeing is believing?”

sense move Spine delay=death

sense move Spine Reflex Reflect

sense move Spine Reflex Reflect

sense move Spine Reflect Reflex Layered

sense move Spine Reflect Reflex Layered

Physiology Organs Neurons Cortex Cells Cortex Layered architectures Cells

sense move Spine Reflect Reflex Layered Prediction Goals Actions errors Actions

Physiology Organs Meta-layers Prediction Goals Actions errors Actions Cortex

Visual Cortex Visual Thalamus 10x ? There are 10x feedback neurons Why?

Visual Cortex Visual Thalamus ? There are 10x feedback neurons Physiology Organs Prediction Goals Actions errors Actions Prediction Goals Conscious perception 10x Why? What are the consequences?

3D +time Simulation Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

Same size?

Same size

Even when you “know” they are the same, they appear different Toggle between this slide and the ones before and after

Same size? Vision: evolved for complex simulation and control, not 2d static pictures Even when you “know” they are the same, they appear different

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing Our most integrated perception of the world

Which blue line is longer?

With social pressure, this one. Standard social psychology experiment.

Chess experts can reconstruct entire chessboard with < ~ 5s inspection can recognize 1e5 distinct patterns can play multiple games blindfolded and simultaneous are no better on random boards (Simon and Gilmartin, de Groot)

Polistes fuscatus can differentiate among normal wasp face images more rapidly and accurately than nonface images or manipulated faces. Polistes metricus is a close relative lacking facial recognition and specialized face learning. Similar specializations for face learning are found in primates and other mammals, although P. fuscatus represents an independent evolution of specialization. Convergence toward face specialization in distant taxa as well as divergence among closely related taxa with different recognition behavior suggests that specialized cognition is surprisingly labile and may be adaptively shaped by species-specific selective pressures such as face recognition.

Fig. 1 Images used for training wasps. M J Sheehan, E A Tibbetts Science 2011;334: Published by AAAS

Meta-layers Physiology Organs Prediction Goals Actions errors Actions Cortex Fast, Limited scope Slow, Broad scope Unfortunately, we’re not sure how this all works. Expertise, learning

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing Our most integrated perception of the world

Physiology Organs Prediction Goals Actions errors Actions Conscious perception Prediction Goals Conscious perception But ultimately, only actions matter.