John Doyle 道陽Jean-Lou Chameau Professor
Control and Dynamical Systems, EE, & BioE
tech1#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
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess
capablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffectiveefficientevolvableextensiblefail transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable robust
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplicitystablestandards
compliantsurvivablesustainabletailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
Requirements on systems and architectures
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess
capablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffectiveefficientevolvableextensiblefail transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable robust
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplicitystablestandards
compliantsurvivablesustainabletailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
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!
Case Study
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…
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffective
evolvableextensiblefail transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandardssurvivable
tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
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)
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffective
evolvableextensiblefail transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandardssurvivable
tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
efficient
robust
sustainable
Sustainable robust + efficient
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffective
evolvableextensiblefail
transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeabl
einteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalit
yportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandardssurvivable
tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
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
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
Evolution Complexity
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) paraphrasingT Dobzhansky
costly
fragile
efficient
robust
Tradeoffs
4x
>2x
wastefulefficient
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. experiments2. data3. theory4. 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
longshort
Impossible
Length is positive(not “waste,” but a cartoon)
cartoon demo
fragile
robust easy
hard
up&short down or long
Gravity is stabilizing
Gravity is destabilizing
Law #1 : MechanicsLaw #2 : Gravity
Universal laws?
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 : MechanicsLaw #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 : MechanicsLaw #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&short down or long
More unstable
Law #1 : MechanicsLaw #2 : GravityLaw #3 : ??Law #4 : ??
fragile
robust
harder
up&short down or long
hardest!
hard harder hardest!
Easy to prove using simple models.
What is sensed matters.
Why?
Why?!?
fragile
robust
harder
up&short down or long
hardest!
Why?
Accident or necessity?Universal laws?
(4) Universal laws +
vision
Act
delay
+ Neuroscience
Balancing an inverted pendulum
Mechanics+Gravity +Light +
expz p
T pz p
Some minimal math details
easy
hard
Law #1 : MechanicsLaw #2 : Gravity
1d motion
Use “conservation laws”
2cos sin
cos sin 0
sinO
M m x ml u
x l g
y x l
M
m
l
l lengthm massM massg gravityu control force
y
x
u
Standard inverted pendulum
2cos sin
cos sin 0
sinO
M m x ml u
x l g
y x l
easy
hard
Law #1 : MechanicsLaw #2 : Gravity
0
O
M m x ml u
x l g
y x l
linearize
1d motion
2cos sin
cos sin 0
sinO
M m x ml u
x l g
y x l
eye
l
n noisee error
Law #3 : Light (vision)
0
O
M m x ml u
x l g
y x l n
hard harder
Easy to prove using simple models.
Why?vision
Act
delay
Law #3 : Light 0
O
M m x ml u
x l g
y x l n
eye
l
N noiseE error
ET j
N
Frequency domain
?T
eye vision
slow
Act
delay
Control
l
N noiseE error
ET j
N
expT p
Universal laws,art, music, Lego
vision
Act
delay
Balancing an inverted pendulum
Mechanics+Gravity +Light +
1p
l
.3s
22 2
2
2
1
( )
1
OO
O
x ls gu D s s Mls M m g
D s s
l l s gy x l u
D s
g m gp r r z
l M l l
0
O
M m x ml u
x l g
y x l n
Laplace transform
(One complex variable)
2 20
1exp ln exp ln
sup
exp lnexp
pT T j d
p
T T j
Tp
T
Fragility two ways (~ Bode* and Zames):
* With key help from Freudenberg and Seron et al
2 20
1exp ln exp ln
sup
pT T j d
p
T T j
exp ln
expT
pT
Amplification (noise to error)
Entropy rate
Energy (L2)
exp ln
expT
pT
Entropy rate
Energy (L2)
exp pt
delay
Before you can
react
time
state
intuition
10
100
Length, m.1 1.5.2
2
exp p1
pl
.3s
Shorter
Fragile
expT p
10
100
Length, m.1 1.5.2
2
exp p
.3s
.2sSlo
wer
1p
l
expT p
10
Length, m.1 1.5.2
2 loglog
0.2 0.4 0.6 0.8 12
4
6
8
10
linearF
ragile exp p
Fragile
Also exponential
in delay!
exp p
M m gp
Ml
10
Length, m.1 1.5.2
2
0.2 0.4 0.6 0.8 12
4
6
8
10
linearF
ragile
exp p
M
m
l
l lengthm massM massg gravity
Idealized model
exp p
M m gp
Ml
10
Length, m.1 1.5.2
2
0.2 0.4 0.6 0.8 12
4
6
8
10
linearF
ragile
exp p
M
m
l
l lengthm massM massg gravity
exp ln
expT
pT
Essential constraint (“law”):
eye vision
Act
delay
Control
l noise
error
P
+
noise
error
C
ET j
N
P
+
noise
error
C
ET j
N
sup sup Re( ) 0|T T j T s s
Max modulus
Proof?
1
exp
( ) ( ) exp
exp
Ms P s s
T M M p p p
T p
P
1
1
( ) ( )
P p T p
M p p
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s s
P
+
noise
error
C
ET j
N
sup sup Re( ) 0|T T j T s s
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s s
Max modulus
Proof?
M “minimum phase” (stably invertible) “all pass”
P
+
noise
error
C
ET j
N
sup sup Re( ) 0|T T j T s s
Max modulus
1
1
( ) ( )
P p T p
M p p
Proof?
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s s
1
so 1
PCT
PCP p T p
P
+
noise
error
C
ET j
N
sup sup Re( ) 0|T T j T s s
Max modulus
Proof?
1
exp
( ) ( ) exp
exp
Ms P s s
T M M p p p
T p
P
1
1
( ) ( )
P p T p
M p p
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s s
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
0l l0l l
exp ln
expT z p
pz pT
Fragility two ways (Bode* and Zames):
Unstable zeros
P
+
noise
error
C
sup sup Re( ) 0|T T j T s s
Proof?
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s zs s
s z
1
exp
( ) ( ) exp
exp
M
s zs P s s
s z
z pT M M p p p
z p
z pT p
z p
P
P
+
noise
error
C
sup sup Re( ) 0|T T j T s s
Proof?
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s zs s
s z
1so 1
& 0 0
PCT
PCP p T p
P z T z
P
+
noise
error
C
sup sup Re( ) 0|T T j T s s
Proof?
1
exp
( ) ( ) exp
exp
M
s zs P s s
s z
z pT M M p p p
z p
z pT p
z p
P
( ) ( ) ( ) ( ) 1
exp
T s M s s j
s zs s
s z
expexp
exl
pn z p
pz p
Tp
T
hardest!
0l l
0l l
exp p
expz p
pz p
10
100
Length, m
.1 1.5.22
1p
l
.3s
hardest!hard
expexp
exl
pn z p
pz p
Tp
T
10
100
Speed 1/
.1 1.5.22
.3s
exp p
Slower
vision
Act
delay
Vary delay?
easyharder
“costly”
fragile
“efficient”
robust
Hard tradeoff
“costly”
fragile
“efficient”
robust
Bad design
Gratuitous fragility
“costly”
fragile
“efficient”
robust
exp ln
expz pT
Tp
z p
Same constraints:Mechanics+Gravity+
Different consequences
Constrained sensing
unconstrained
The nature of “laws”
“costly”
fragile
“efficient”
robust easy
hard
harder hardest!
up&short down or long
Different consequences
wasteful
fragile
efficient
robust Ideal
Impossible (law)
Architecture
Universal laws and architectures
Flexibly achieves what’s possible
Cortex
eye vision
Actslow
delay
VOR
fast
Object motion
Head motion
Cerebellum
+
ErrorTune gain
gain
AOS
AOS = Accessory Optical system
noise
Act
Highly evolved (hidden)
architecture
Cortex
eye vision
Actslow
delay
VOR
fast
Object motion
Head motion
Cerebellum
+
ErrorTune gain
gain
AOS
AOS = Accessory Optical system
noise
Act
slowest
Cortex
eye vision
Actslow
delay
VOR
fast
Object motion
Head motion
Cerebellum
+
ErrorTune gain
gain
AOS
AOS = Accessory Optical system
noise
Act
Delays everywhere
Distributed control
slowest
Cortex
eye vision
Actslow
delay
VOR
fast
Object motion
Head motion
Cerebellum
+
ErrorTune gain
gain
AOS
noise
Act
slowestHeterogeneous
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
InsightAccessibleVerifiable
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.
exp ln T z p
z pT
Law #1 : Chemistry (vs mechanics)Law #2 : Autocatalysis (vs gravity)
( RHP p and z)
exp ln T z p
z pT
Law #3:
k
z p
z p
10-1
100
10110
0
101
too fragile
complex
No tradeoff
expensive
fragile
Law #1 : ChemistryLaw #2 : Autocatalysis
( RHP p and z)
Law #3:
exp ln T z p
z pT
Metabolic overhead to make enzymes
fragile
Robust Efficiency in Energy Supply
Robust to in supply and demand
k
z p
z p
10-1
100
10110
0
101
too fragile
complex
No tradeoff
Metabolic overhead to make enzymes
fragile
Robust Efficiency in Energy Supply
Robust to in supply and demand
exp ln T z p
z pT
Core metabolism
Inside every cell (1030)
Metabolic pathways
No architecture
Catabolism
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Metabolic pathways
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Pre
curs
ors
Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Huge Variety
Same 12
in all cells
Same 8
in all cells
100
» same
in all
organism
s
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Huge Variety
Same 12
in all cells
Same 8
in all cells
100
» same
in all
organism
s
Extremely conserved
core
Pre
curs
ors
Genes
Co-factorsFatty acidsSugars
Nucleotides
Amino Acids
Polymerization and complex
assemblyP
roteinsP
recu
rsor
s
Autocatalytic feedback
Taxis and transport
Nut
rien
ts
Core metabolism
DNA replication
Trans*
Cat
abol
ism
Carriers
100 104 to ∞in one
organisms
Huge Variety
12
8
EfficientRobust
Evolvable
Constrained
DeconstrainedDeconstrained
DiverseDiverse
Inside every cell
Layered architecture
Catabolism
Precursors
Carriers
Biosynthesis
Cat
abol
ism
Pre
curs
ors
Carriers
Inside every cell
Layered architecture
Bio
synt
hesi
sCo-factors
Fatty acids
Sugars
NucleotidesAmino Acids
BiosyntheticPathways
Catabolism
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Inside every cell
Core metabolic bowtieLayered architecture
food
Glucose
Oxygen
OrgansTissuesCellsMolecules
Universal metabolic system
Blood
EfficientRobust
Evolvable
food
Glucose
Oxygen
OrgansTissuesCellsMolecules
Blood
EfficientRobust
Evolvable
Constrained
DeconstrainedDeconstrained
EfficientRobust
Evolvable
Constrained
DeconstrainedDeconstrained
DiverseDiverse
Catabolism
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Inside every cell
Core metabolic bowtieLayered architecture
Catabolism
Pre
curs
ors
Carriers
Catabolism
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
ATP
NADH
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
Pre
curs
ors
metabolites
Enzymatically catalyzed reactions
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
Oxa
Cit
ACA
Enzymes (implement) catalyze
(virtual) reactions
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
Oxa
Cit
ACA
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
ATP
Autocatalytic
NADH
produced
consumedRest of cell
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Autocatalysis
Control
Feedbacks
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Unreadable
Why so complex?
source receiver
signalinggene expression
metabolismlineage
Biological pathways
source receiver
control
energy
materials
signalinggene expression
metabolismlineage
More complex
feedback
Chandra, Buzi, and Doyle
UG biochem, math, control theory
Most important paper so far.
Universal laws?
10
100
Length, m
.1 1.5.22
k10-1
100
10110
0
101
expensive
fragile
exp ln
expT z p
pz pT
wasteful
fragile
efficient
robust Ideal
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
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
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
F6P
F1-6BP
Gly3p
13BPG
3PG
ATP
Autocatalysis
Control
PEP2PG Pyr
Minimal model?
F6P
F1-6BP
Gly3p
13BPG
3PG
ATP
F6P
F1-6BP
Gly3p
13BPG
3PG
ATP
ControlPlus Autocatalytic Feedback
PEP Pyr
Rest of cell
Minimal model ~1 equilibrium 2 metabolites 3 “reactions”
F6P
F1-6BPATP
F6P
F1-6BPGly3p
13BPG
3PG
ATP
produced
consumed
Cell
PEPPyr
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. Instability2. 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
enzymes
enzy
mes
Efficient = low metabolic overhead low enzyme amount
enzymes catalyze reactions, another
source of autocatalysis
ATP
Rest of cell
Protein biosyn
energy
enzymes
enzymes
enzy
mes
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
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Autocatalysis
Control
ATP
Rest of cell
Reaction 2 (“PK”)
Reaction 1 (“PFK”)
Protein biosyn
Autocatalysis
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
k
z p
z p
10-1
100
10110
0
101
too fragile
complex
No tradeoff
Metabolic overhead to make enzymes
fragile
Robust Efficiency in Energy Supply
Robust to in supply and demand
exp ln T z p
z pT
k
z p
z p
10-1
100
10110
0
101
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
k
z p
z p
10-1
100
10110
0
101
too fragile
complex
No tradeoff
Metabolic overhead
fragile
Robust to in supply and demand
Uncertain k
M
m
l
l lengthm massM massg gravityu control force
y
x
u
Standard inverted pendulum
2
sin
cos sin 0
cos sin
Oy x l n
x l g
M m x ml u
Uncertainty?
eye vision
Act
delay
Control
l noise
error
In our model?In our brain?In our brain’s model?
• Parameters• Noise• Unmodeled dynamics
2
sin
cos sin 0
cos sin
Oy x l n
x l g
M m x ml u
Uncertainty?
In our model?In our brain?In our brain’s model?
• Parameters (real)• Noise (additive)• Unmodeled dynamics (complex)• Nonlinear dynamics
2
sin
cos sin 0
cos sin
Oy x l n
x l g
M m x ml u
Uncertainty?
AnalysisLimits/lawsSynthesis
controls
external disturbances
heart rateventilation
errors
O2BP
energy
Homeostasis and HRV
0 100 200 300 4000 100 200 300 400
40
60
80
100
120
140
160
180
HR
High mean, low variability
Low mean, high variability
The persistent mysteryYoung, fit, healthy more extreme
Seeking mechanistic explanations
sec
0 100 200 300 4000 100 200 300 400
40
60
80
100
120
140
160
180
HRHigh mean, low variability
Low mean, high variability
sec
controls
external disturbances
heart rateventilation
errors
O2BP
energy
Finally!
Homeostasis and HRV
sensor
controls
external disturbances
heart rateventilationvasodilationcoagulationinflammationdigestionstorage…
errors
O2BPpHGlucoseEnergy storeBlood volume…
infection
traumaenergy
Homeostasis and robust efficiency
internal noise
heart beatbreath
Mechanistic physiology
The main tradeoff: Robust efficiency
Mechanistic physiology + rigorous math and stats
sensor
controls
external disturbances
heart rateventilationvasodilationcoagulationinflammationdigestionstorage…
errors
O2BPpHGlucoseEnergy storeBlood volume…
infection
traumaenergy
Homeostasis
internal noise
heart beatbreath
costly
fragile
efficient
robust
Tradeoffs:• physiology • evolution • ecologically
relevant
Mechanistic physiology + rigorous math and stats
controls
external disturbances
heart rateventilation
errors
O2BP
energy
Homeostasis
Muscle
Lung
W
H
EV
CBF
H
sR
pump
pump
dilate
dilate
high variability
Disturbance: W
SaO2
Pas
O2
CBF
Health low variability
Why?
flow
pressure
O2
O2
Minimal cartoon
pump
pump
dilate
dilate
high variability
Disturbance:run
Healthy homeostasis
low variability
flow
pressure
O2
O2
Minimal cartoon
actuators
regulated variables
pump
pump
dilate
dilate
high variability Healthy homeostasis
low variability
flow
pressure
O2
O2
Minimal cartoon
+
actuators
regulated variablesDisturbance
0 100 200 300 4000 100 200 300 400
40
60
80
100
120
140
160
180
HR
High mean, low variability
Low mean, high variability
The persistent mysteryYoung, 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
l noise
error
Control actuators
Universal laws
10
100
Length, m
.1 1.5.22
k10-1
100
10110
0
101
expensive
fragile
wasteful
fragile
efficient
robust
sensor
controlsheart rateventilationvasodilationcoagulationinflammationdigestionstorage…
errors
O2BPpHGlucoseEnergyBlood vol
infection
traumaenergy
heart beatbreath
Homeostasis
Universal laws
10
100
Length, m
.1 1.5.22
k10-1
100
10110
0
101
expensive
fragile
wasteful
fragile
efficient
robust
sensor
controlsheart rateventilationvasodilationcoagulationinflammationdigestionstorage…
errors
O2BPpHGlucoseEnergyBlood vol
infection
traumaenergy
heart beatbreath
Homeostasis
Do these oscillations have a function/purpose?
Is loss of actuator variability a precursor?
eye vision
Act
delay
Control
l noise
error ET j
N
P
+
noise
error
C
Understand this more deeply?
+ Neuroscience
Mechanics+Gravity +
Light +Control theory
exp ln
expT z p
pz pT