John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca Universal laws and architectures: Theory and lessons from brains, bugs, nets, grids, planes, docs, fire, bodies, fashion, earthquakes, turbulence, music, buildings, cities, art, running, throwing, Synesthesia, spacecraft, statistical mechanics

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 fail transparent fast 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 fail transparent fast 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 When concepts fail, words arise. Mephistopheles, Faust, Goethe Mephistopheles. …Enter the templed hall of Certainty. Student. Yet in each word some concept there must be. Mephistopheles. Quite true! But don't torment yourself too anxiously; For at the point where concepts fail, At the right time a word is thrust in there…

When concepts fail, words arise. Mephistopheles, Faust, Goethe Concrete case studies Theorems Sorry, still too many words and slides. Hopefully read later?

When concepts fail, words arise. Mephistopheles, Faust, Goethe Concrete case studies Theorems “Laws and Architecture” Few words more misused Few concepts more confused What’s the best/simplest fix?

Reality is a crutch for people who can’t do math. Anon, Berkeley, 70’s Concrete case studies Theorems and words

Brains Nets/Grids (cyberphys ) Bugs (microbes, ants) Medical physiology Lots of aerospace Wildfire ecology Earthquakes Physics: –turbulence, –stat mech (QM?) “Toy”: –Lego –clothing, fashion Buildings, cities Synesthesia Fundamentals!

Focus today: Neuroscience + People care + Live demos Cell biology (esp. bacteria) + Perfection  Some people care Internet (of everything) (& Cyber-Phys) + Understand the details - Flawed designs -Everything you’ve read is wrong (in science)* Medical physiology (esp. HRV) + People care, somewhat familiar - Demos more difficult * Mostly high impact “journals”

Focus today: Neuroscience + People care + Live demos Cell biology (esp. bacteria) + Perfection  Some people care Internet (of everything) (& Cyber-Phys) + Understand the details - Flawed designs -Everything you’ve read is wrong (in science)* Medical physiology + People care, somewhat familiar - Demos more difficult * Mostly high impact “journals”

Glass 3.0 FaceWorld Siri 3.0 What we want to build but can’t, yet.

The zombie apocalypse is already here…

accessible accountable accurate adaptable administrable affordable auditable autonomy available compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective evolvable extensible fail transparent fast 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 simple stable standards survivable tailorable testable timely traceable ubiquitous understandable upgradable usable efficient robust sustainable Sustainable  robust + efficient

Priorities Functionality (behavior, semantics) Robustness –Uncertain environment and components –Fast (sense, decide, act) –Flexible (adaptable, evolvable) Efficiency –Energy –Other resources (make and maintain)

Simple, apparent, obvious Functionality Robustness –Uncertain environment and components –Fast (sense, decide, act) –Flexible (adaptable, evolvable) Efficiency Hidden

Complexity  Robustness Functionality (behavior, semantics) Robustness –Uncertain environment and components –Fast (sense, decide, act) –Flexible (adaptable, evolvable) Efficiency –Energy –Other resources (make and maintain)

accessible accountable accurate adaptable administrable affordable auditable autonomy available compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective evolvable extensible fail transparent fast 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 simple stable standards survivable tailorable testable timely traceable ubiquitous understandable upgradable usable efficient robust sustainable Sustainable  robust + efficient

accessible accountable accurate adaptable administrable affordable auditable autonomy available compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective evolvable extensible fail transparent fast fault-tolerant fidelity flexible inspectable installable Integrity interchangeabl e interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonalit y portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable safety scalable seamless self-sustainable serviceable supportable securable simple stable standards survivable tailorable testable timely traceable ubiquitous understandable upgradable usable efficient robust sustainable Simple dichotomous tradeoff pairs PCA  Principal Concept Analysis wasteful fragile efficient robust With function given

wasteful fragile efficient robust Actual Ideal The main tradeoff

Efficiency/instability/layers/feedback Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science) New efficiencies but also instability/fragility New distributed/layered/complex/active control Live demo?

costly fragile efficient robust Tradeoffs (swim/crawl to run/bike) Function= Locomotion

costly fragile efficient robust Tradeoffs 4x >2x

costly fragile efficient robust

costly fragile efficient robust How does a layered architecture, including an “executive,” trade off robustness and efficiency?

costly fragile efficient robust How does a layered architecture, including an “executive,” trade off robustness and efficiency?

wasteful fragile efficient robust Ideal Impossible (law) Actual Universal laws

wasteful fragile efficient robust Ideal Impossible (law) Actual The risk

wasteful fragile efficient robust Ideal Impossible (law) Architecture Universal laws and architectures Flexibly achieves what’s possible

wasteful fragile efficient robust Ideal Impossible (law) Architecture Universal laws and architectures Our heroes EvolutionComplexity

Efficiency/instability/layers/feedback Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science) All create new efficiencies but also instabilities Needs new distributed/layered/complex/active control Major transitions

“Nothing in biology makes sense except in the light of evolution.” T Dobzhansky “Nothing in evolution makes sense except in the light of biology.” Tony Dean (U Minn) paraphrasing T Dobzhansky

costly fragile efficient robust Tradeoffs 4x >2x

costly fragile efficient robust Tradeoffs

costly fragile efficient robust Tradeoffs

wasteful efficient Materials and energy have many “universal conservation laws” that limit achievable efficiency. Perfect efficiency would have zero waste. Impossible (law)

fragile robust Robustness also has “universal conservation laws” that are less familiar… …though their consequence are surprisingly ubiquitous. Impossible (law)

Brains Nets/Grids (cyberphys ) Bugs (microbes, ants) Medical physiology Lots of aerospace Wildfire ecology Earthquakes Physics: –turbulence, –stat mech (QM?) “Toy”: –Lego –clothing, fashion Buildings, cities Synesthesia fragile robust

Neuroscience + People care +Live demos! 1.experiments 2.data 3.theory 4.universals

costly fragile efficient robust Tradeoffs 4x >2x Simpler demo?

costly fragile efficient robust hard harder A convenient cartoon easy Function= Movement

“costly” fragile “efficient?” robust easy hard harder A convenient cartoon demo Function= Movement

“costly” fragile “efficient” robust easy hard harder up&short cartoon demo down or long

long short Impossible Length is positive (not “waste,” but a cartoon) cartoon demo

fragile robust easy hard up&shortdown or long Gravity is stabilizing Gravity is destabilizing Law #1 : Mechanics Law #2 : Gravity Universal laws? Cerebellum

Efficiency/instability/layers/feedback Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science) New efficiencies but also instabilities New distributed/layered/complex/active control stabilizing destabilizing cartoon demo

Gravity is stabilizing Gravity is destabilizing Law #1 : Mechanics Law #2 : Gravity We think of mechanics and gravity as “obeying universal laws.” But both “universal” and “law” are confused and overloaded, so unfortunate terminology. Universal laws?

Gravity is stabilizing Gravity is destabilizing Law #1 : Mechanics Law #2 : Gravity We think of mechanics and gravity as “obeying universal laws.” (Generally: constraints) But the consequences (even of gravity) depend on other constraints. Universal laws?

fragile robust harder up&shortdown or long More unstable Law #1 : Mechanics Law #2 : Gravity Law #3 : ?? Law #4 : ??

fragile robust harder up&shortdown or long hardest!

hard harder hardest! Easy to prove using simple models. What is sensed matters. Why? Why?!?

fragile robust harder up&shortdown or long hardest! Why? Accident or necessity? Universal laws?

(4) Universal laws + vision Act delay + Neuroscience Balancing an inverted pendulum Mechanics+ Gravity + Light + Some minimal math details

easy hard Law #1 : Mechanics Law #2 : Gravity 1d motion Use “conservation laws”

M m l l length mmass Mmass ggravity ucontrol force y x  u Standard inverted pendulum

easy hard Law #1 : Mechanics Law #2 : Gravity linearize 1d motion

eye n noise e error Law #3 : Light (vision)

hard harder Easy to prove using simple models. Why? vision Act delay Law #3 : Light

eye N noise E error Frequency domain

eye vision slow Act delay Control N noise E error

Universal laws, art, music, Lego vision Act delay Balancing an inverted pendulum Mechanics+ Gravity + Light +

Laplace transform (One complex variable)

Fragility two ways (~ Bode* and Zames): * With key help from Freudenberg and Seron et al

Amplification (noise to error) Entropy rate Energy (L2)

Entropy rate Energy (L2) Before you can react time state intuition

Length, m Shorter Fragile

Length, m Slower

10 Length, m loglog linear Fragile Also exponential in delay!

10 Length, m linear Fragile M m l l length mmass Mmass ggravity Idealized model

10 Length, m linear Fragile M m l l length mmass Mmass ggravity Essential constraint (“law”):

eye vision Act delay Control noise error P + noise error C Cerebellum

P + noise error C Max modulus Proof?

P + noise error C Max modulus Proof? M “minimum phase” (stably invertible)  “all pass”

P + noise error C Max modulus Proof?

P + noise error C Max modulus Proof?

hard harder hardest! Easy to prove using simple models. What is sensed matters. Why?

hard harder hardest! What is sensed matters. Unstable poles Unstable zeros

Fragility two ways (Bode* and Zames): Unstable zeros

P + noise error C Proof?

P + noise error C Proof?

P + noise error C Proof?

hardest!

Length, m hardest! hard

Speed  1/  Slower vision Act delay Vary delay?

vision Act delay Holds for all controllers. A “law” about intrinsic problem difficulty (a la Turing).

Length, m easy hard harder

easy harder “costly” fragile “efficient” robust Hard tradeoff

“costly” fragile “efficient” robust Bad design Gratuitous fragility

“costly” fragile “efficient” robust Same constraints: Mechanics+ Gravity+ Different consequences Constrained sensing unconstrained The nature of “laws”

“costly” fragile “efficient” robust easy hard harder hardest! up&shortdown or long Different consequences

wasteful fragile efficient robust Ideal Impossible (law) Architecture Universal laws and architectures Flexibly achieves what’s possible

Cortex eye vision Act slow delay VOR fast Object motion Head motion Cerebellum + Error Tune gain gain AOS AOS = Accessory Optical system noise Act Highly evolved (hidden) architecture

Cortex eye vision Act slow delay VOR fast Object motion Head motion Cerebellum + Error Tune gain gain AOS AOS = Accessory Optical system noise Act slowest

Cortex eye vision Act slow delay VOR fast Object motion Head motion Cerebellum + Error Tune gain gain AOS AOS = Accessory Optical system noise Act Delays everywhere Distributed control slowest

Cortex eye vision Act slow delay VOR fast Object motion Head motion Cerebellum + Error Tune gain gain AOS noise Act slowest Heterogeneous delays everywhere

Efficiency/instability/layers/feedback Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science) Major transitions How universal? Very.

Chandra, Buzi, and Doyle UG biochem, math, control theory Insight Accessible Verifiable

Glycolytic oscillations Exhaustively studied –Extensive experiments and data –Detailed models and simulations –Great! But all just deepen the mystery Perfectly illustrates “conservation law” Without which? Bewilderment.

Law #1 : Chemistry (vs mechanics) Law #2 : Autocatalysis (vs gravity) (  RHP p and z) Law #3:

too fragile complex No tradeoff expensive fragile Law #1 : Chemistry Law #2 : Autocatalysis (  RHP p and z) Law #3:

Metabolic overhead to make enzymes fragile Robust Efficiency in Energy Supply Robust to  in supply and demand

too fragile complex No tradeoff Metabolic overhead to make enzymes fragile Robust Efficiency in Energy Supply Robust to  in supply and demand

Core metabolism Inside every cell (  ) Metabolic pathways No architecture

Catabolism Precursors Carriers Co-factors Fatty acids Sugars Nucleotides Amino Acids Metabolic pathways

Co-factors Fatty acids Sugars Nucleotides Amino Acids Catabolism Precursors Taxis and transport Nutrients Carriers Core metabolism Huge Variety Same 12 in all cells Same 8 in all cells  100  same in all organisms

Co-factors Fatty acids Sugars Nucleotides Amino Acids Catabolism Taxis and transport Nutrients Carriers Core metabolism Huge Variety Same 12 in all cells Same 8 in all cells  100  same in all organisms Extremely conserved core Precursors

Genes Co-factors Fatty acids Sugars Nucleotides Amino Acids Polymerization and complex assembly Proteins Precursors Autocatalytic feedback Taxis and transport Nutrients Core metabolism DNA replication Trans* Catabolism Carriers  100  10 4 to  ∞ in one organisms Huge Variety 12 8

Efficient Robust Evolvable Constrained Deconstrained Diverse

Inside every cell Layered architecture Catabolism Precursors Carriers Biosynthesis

Catabolism Precursors Carriers Inside every cell Layered architecture Biosynthesis Co-factors Fatty acids Sugars Nucleotides Amino Acids Biosynthetic Pathways

Catabolism Precursors Carriers Co-factors Fatty acids Sugars Nucleotides Amino Acids Inside every cell Core metabolic bowtie Layered architecture

food Glucose Oxygen Organs Tissues Cells Molecules Universal metabolic system Blood Efficient Robust Evolvable

food Glucose Oxygen Organs Tissues Cells Molecules Blood Efficient Robust Evolvable Constrained Deconstrained

Efficient Robust Evolvable Constrained Deconstrained Diverse

Catabolism Precursors Carriers Co-factors Fatty acids Sugars Nucleotides Amino Acids Inside every cell Core metabolic bowtie Layered architecture

Catabolism Precursors Carriers

Catabolism TCA Pyr Oxa Cit ACA Gly G1P G6P F6P F1-6BP PEP Gly3p 13BPG 3PG 2PG ATP NADH

TCA Pyr Oxa Cit ACA Gly G1P G6P F6P F1-6BP PEP Gly3p 13BPG 3PG 2PG Precursors metabolites

Enzymatically catalyzed reactions TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG Oxa Cit ACA

Enzymes (implement) catalyze (virtual) reactions TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG Oxa Cit ACA

TCA Pyr Oxa Cit ACA Gly G1P G6P F6P F1-6BP PEP Gly3p 13BPG 3PG 2PG ATP Autocatalytic NADH produced consumed Rest of cell

TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG ATP NADH Oxa Cit ACA Autocatalysis Control Feedbacks

TCA Pyr Oxa Cit ACA Gly G1P G6P F6P F1-6BP PEP Gly3p 13BPG 3PG 2PG

TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG ATP NADH Oxa Cit ACA Unreadable Why so complex?

sourcereceiver signaling gene expression metabolism lineage Biological pathways

sourcereceiver control energy materials signaling gene expression metabolism lineage More complex feedback

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

Universal laws? Length, m expensive fragile wasteful fragile efficient robust Ideal

TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG ATP NADH Oxa Cit ACA Autocatalysis Control Feedbacks Robust=maintain energy charge w/fluctuating cell demand Efficient=minimize metabolic waste and overhead

TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG ATP NADH Oxa Cit ACA F6P F1-6BP Gly3p 13BPG 3PG ATP Autocatalysis Control PEP 2PG Pyr Minimal model?

F6P F1-6BP Gly3p 13BPG 3PG ATP F6P F1-6BP Gly3p 13BPG 3PG ATP Control Plus Autocatalytic Feedback PEP Pyr Rest of cell Minimal model ~1 equilibrium 2 metabolites 3 “reactions”

F6P F1-6BP ATP

F6P F1-6BP Gly3p 13BPG 3PG ATP produced consumed Cell PEP Pyr lump Minimal model ~1 equilibrium 2 metabolites 3 “reactions”

Fragile Robust ATP Rest of cell energy ATP disturbance Robust = Maintain energy (ATP concentration) despite demand fluctuation Hard tradeoff in glycolysis

Fragile Robust disturbance Robust  Disturbance rejection  Accurate What makes this hard? 1.Instability (autocatalysis) 2.Delay (enzyme amount) Accurate vs sloppy

Fragile Robust What makes this hard? 1.Instability 2.Delay The CNS must cope with both!

ATP Reaction 2 (“PK”) Reaction 1 (“PFK”) energy enzymes catalyze reactions Rest of cell

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

ATP Rest of cell Protein biosyn energy enzymes Efficient = low metabolic overhead  low enzyme amount (  slow reactions) enzymes catalyze reactions, another source of autocatalysis Can’t make too many enzymes here, need to supply rest of the cell. reaction rates  enzyme amount

TCA Gly G1P G6P F6P F1-6BP PEPPyr Gly3p 13BPG 3PG 2PG ATP NADH Oxa Cit ACA Autocatalysis Control ATP Rest of cell Reaction 2 (“PK”) Reaction 1 (“PFK”) Protein biosyn Autocatalysis

Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science) New forms important in most transitions Major and poorly studies source of instability Major transitions

too fragile complex No tradeoff Metabolic overhead to make enzymes fragile Robust Efficiency in Energy Supply Robust to  in supply and demand

too fragile complex No tradeoff Metabolic overhead fragile Robust to  in supply and demand Uncertain k

What (some) reviewers say “…to establish universality … is simply wrong. It cannot be done… … a mathematical scheme without any real connections to biological or medical… …universality is well justified in physics… for biological and physiological systems …a dream …never be realized, due to the vast diversity in such systems. …does not seem to understand or appreciate the vast diversity of biological and physiological systems… …a high degree of abstraction, which …make[s] the model useless …

What (some) reviewers say “…to establish universality … is simply wrong. It cannot be done… … a mathematical scheme without any real connections to biological or medical… …universality is well justified in physics… for biological and physiological systems …a dream …never be realized, due to the vast diversity in such systems. …does not seem to understand or appreciate the vast diversity of biological and physiological systems… …a high degree of abstraction, which …make[s] the model useless … If you agree You’re in good company Stay off commercial aircraft

too fragile complex No tradeoff Metabolic overhead fragile Robust to  in supply and demand Uncertain k

M m l l length mmass Mmass ggravity ucontrol force y x  u Standard inverted pendulum Uncertainty?

eye vision Act delay Control noise error In our model? In our brain? In our brain’s model? Parameters Noise Unmodeled dynamics Uncertainty?

In our model? In our brain? In our brain’s model? Parameters (real) Noise (additive) Unmodeled dynamics (complex) Nonlinear dynamics Uncertainty? Analysis Limits/laws Synthesis

controls external disturbances heart rate ventilation errors O2 BP energy Homeostasis and HRV

HR High mean, low variability Low mean, high variability The persistent mystery Young, fit, healthy  more extreme Seeking mechanistic explanations sec

HR High mean, low variability Low mean, high variability sec controls external disturbances heart rate ventilation errors O2 BP energy Finally! Homeostasis and HRV

sensor controls external disturbances heart rate ventilation vasodilation coagulation inflammation digestion storage … errors O2 BP pH Glucose Energy store Blood volume … infection trauma energy Homeostasis and robust efficiency internal noise heart beat breath Mechanistic physiology

The main tradeoff: Robust efficiency Mechanistic physiology + rigorous math and stats

sensor controls external disturbances heart rate ventilation vasodilation coagulation inflammation digestion storage … errors O2 BP pH Glucose Energy store Blood volume … infection trauma energy Homeostasis internal noise heart beat breath costly fragile efficient robust Tradeoffs: physiology evolution ecologically relevant Mechanistic physiology + rigorous math and stats

controls external disturbances heart rate ventilation errors O2 BP energy Homeostasis

Muscle Lung W W H CBF H pump dilate high variability Disturbance: W SaO 2 P as O2O2 CBF Health low variability Why? flow pressure O2O2 O2O2 Minimal cartoon

pump dilate high variability Disturbance: run Healthy homeostasis low variability flow pressure O2O2 O2O2 Minimal cartoon actuators regulated variables

pump dilate high variability Healthy homeostasis low variability flow pressure O2O2 O2O2 Minimal cartoon + actuators regulated variables Disturbance

HR High mean, low variability Low mean, high variability The persistent mystery Young, fit, healthy  more extreme Seeking mechanistic explanations sec

actuators (controls) + (outputs) regulated variables Disturbance low variability errors + large disturbances  high variability controls Universals

low variability errors + large noise/delay  high variability controls Universals Is loss of actuator variability a precursor of a crash? eye vision Act delay Control noise error Control actuators

Universal laws Length, m expensive fragile wasteful fragile efficient robust sensor controls heart rate ventilation vasodilation coagulation inflammation digestion storage … errors O2 BP pH Glucose Energy Blood vol infection trauma energy heart beat breath Homeostasis

Universal laws Length, m expensive fragile wasteful fragile efficient robust sensor controls heart rate ventilation vasodilation coagulation inflammation digestion storage … errors O2 BP pH Glucose Energy Blood vol infection trauma energy heart beat breath Homeostasis Do these oscillations have a function/purpose? Is loss of actuator variability a precursor?

eye vision Act delay Control noise error P + noise error C Understand this more deeply? + Neuroscience Mechanics+ Gravity + Light + Control theory