john doyle john g braun professor control and dynamical...

John DoyleJohn G Braun Professor

Control and Dynamical Systems, EE, BioECaltech

Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011

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.


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.

Familiar and accessible case studies are used to illustrate

concepts of robustness, organization, and architecture

(modularity and protocols) that are central to understanding

complex networks.

These essential organizational features are hidden during normal

function of a system, but fundamental for understanding the

nature, design, and function of complex biologic and technologic

systems.


Starting points

large,thin,nonconvex

lobster somatogastric ganglia

Helen S. Mayberg, MDProfessor, Psychiatry and Neurology

Dorothy C. Fuqua Chair in Psychiatric Neuroimaging and Therapeutics

Emory University School of Medicine

Last week

moF11pACC24

mF9/10

PCCMCC

PF9/46 Par40PM6

sACC25

hth bstema-ins

amg mb-vta

hc

na-vst thal

SalienceMotivation

Moodstate

EmotionRegulation

Cognitionattention-appraisal-action

Interoceptiondrive-autonomic-circadianMayberg, Br Med Bul 65:193-207, 2003

Mayberg, J Clin Invest 119:717, 2009

Putative “Depression” Network ~ 2001defined using functional imaging

PF

P

Medications

Cognitive

Behavior

Therapy

Where might

DBS fit into

this schema?

Mayberg, J Neuropsych Clin NS, 1997

cg25

Tracts thru SeedCg25WM Target Fiber Assignment by

Continuous Tracking

along adjacent pixels

Fornix

Probablistic Tractography

DTI: Blind ACC ParcellationSVD 2 clusters: sACC Pacc (n=18)

Cingulate

ROI

Johansen-Berg et al

Cerebral Cortex 2008

a-hc

Overlap

Unique inferior ROI

Unique superior ROI

na-hth

Fx

Diffusion Tensor

Imaging

Rethinking Critical PathwaysMapping Fibers of Passage thru SCC25

cg

mF

cg

25

both

4dmF9MCC

nA

25

3

oF11

nA

25

nA

Differences between

Adjacent contacts

mACCmF9

oF11

mF10

Define tracts affected by stimulation

4

3

2

1

nAc

Am/hcSt-Thdm-Th

HcAm

Midline Cortex

limbicIns

oF11 mF10

pACC MCC PCC

vtaCg25

Cingulum

Hth DR LC pag

nac-vs

brainstem

mF9

Model

4321

Post-op CT/MRI merge

Rethinking Critical PathwaysMapping Fibers of Passage thru SCC25

Sensors

Effecto

rs

Reacti

ve

Ad

ap

tive

Co

nte

xtu

al

So

ma

Value/utility

Long Term Memory

Allostatic control

Needs

Internal states

(valence/arousal)

World

Working memory

Sequence/Interval

memory

Episodic memory

PerceptionAction

shaping/

selection

BehaviorsSensation

Plans

Autobiographical memory / Meta learning

Event

memory

Goals

The

Distributed

Adaptive

Control

Architecture

Verschure & Voegtlin (1998) Neural Netw

Verschure et al (1992) Rob. Aut. Sys.

Verschure & Althaus (2003) Cogn. Sci. (27) 561

Verschure et al (2003) Nature (425) 620

Duff et al (2010) Neurocomputing

Mathews et al (2009;2010) IROS09;ICRA10

Verschure & Coolen (1991) Network

Duff et al (2011) Br.Res.Bull.

Sanches et al (2010) Adv Compl Sys

Eng et al (2003;2005) ICRA; IEEE Tr Sys, Man,

Paul

Verschure

Sensors

Effecto

rs

Reactive

Adaptive

Contextual

Soma

World

Layered

Distributed

Adaptive

Control

Architecture

Sensors

Effecto

rs

Reacti

ve

Ad

ap

tiv

eC

on

textu

al

So

ma

Value/utility

Long Term Memory

Allostatic control

Needs

Internal states

(valence/arousal)

World

Working memory

Sequence/Interval

memory

Episodic memory

PerceptionAction

shaping/

selection

BehaviorsSensation

Plans


Self:

Interoception

World:

Exteroception

Action

Event

memory

Goals

The Distributed

Adaptive

Control

Architecture

Weak 1st

person

perspective

1PP

minimally

phenomenal self

(MPS)

Sensors

Effecto

rs

Reacti

ve

Ad

ap

tive

Co

nte

xtu

al

So

ma

Value/utility

Long Term Memory

Allostatic control

Needs

Internal states

(valence/arousal)

World

Working memory

Sequence/Interval

memory

Episodic memory

PerceptionAction

shaping/

selection

BehaviorsSensation

Plans


Self:

Interoception

World:

Exteroception

Action

Event

memory

Goals

Levels of Conciousness

following Metzinger

(2003)

The Distributed

Adaptive

Control

Architecture

“Laws” and architectures

• Theory of hard limits, constraints,… (“laws” )

• Theory of “architecture”?

– From platforms to

– “systems of systems” to

– Architecture

• Case studies: Internet, turbulence, smartgrid, cell biology, wildfire ecology, earthquakes, stat mech, brain architecture, UAVs, …

IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS,

JULY 2010, Alderson and Doyle

Robust Fragile

Human complexity

Metabolism

Regeneration & repair

Healing wound /infect

Obesity, diabetes

Cancer

AutoImmune/Inflame

Robust Fragile

Mechanism?

Metabolism



Fat accumulation

Insulin resistance

Proliferation

Inflammation

Obesity, diabetes

Cancer

AutoImmune/Inflame

Fat accumulation

Insulin resistance

Proliferation

Inflammation

Robust Fragile

What’s the difference?

Metabolism



Obesity, diabetes

Cancer

AutoImmune/Inflame

Accident or necessity?

Fat accumulation

Insulin resistance

Proliferation

Inflammation

Fluctuating

energy

Static

energy

Robust Fragile

What’s the difference?

Metabolism



Obesity, diabetes

Cancer

AutoImmune/Inflame

Fat accumulation

Insulin resistance

Proliferation

Inflammation

Controlled

Dynamic

Uncontrolled

Chronic

Low mean

High variabilityHigh mean

Low variability

Robust Fragile

Restoring robustness

Controlled

Dynamic

Uncontrolled

Chronic

Low mean

High variabilityHigh mean

Low variability

Robust

Metabolism



Fat accumulation

Insulin resistance

Proliferation

Inflammation

Fluctuating

energy

Controlled

Dynamic

Low mean

High variability

Mechanism?

Brain

Heart

Muscle

Liver

GI

Glu

Triglyc

Fat

Glyc

Glyc

FFA

Glycerol

Oxy

Lac/ph

Out

fast slow

high

low

pri

ori

tydynamics

Control?

• Energy• Inflammation• Coagulation

Evolved for large energy variation and

moderate trauma

source receiver

signaling

gene expression

metabolism

lineage

Biological

pathways

Brain

Heart

Muscle

Liver

GI

Glu

Triglyc

Fat

Glyc

Glyc

FFA

Glycerol

Oxy

Lac/ph

Out

fast slow

high

low

pri

ori

tydynamics

source receiverBiological

pathways

source receiver

control

energy

materials

signaling

gene expression

metabolism

lineage

More

complex

feedback

food intake

Glucose

Oxygen

Amino acids

Fatty acids

Organs

Tissues

Cells

Molecules

Universal metabolic system

Blood

Peter Sterling and Allostasis

food intake

Organs

Tissues

Cells

Molecules

Robust

Highly

variable

supply

Highly

variable

demand

Evolvableevolving

diet

evolving

function

Efficient

Organs

Tissues

Cells

Molecules

Highly

variable

demand

food intake

Highly

variable

supply

evolving

dietevolving

function

Glucose

Oxygen

Blood

Conserved

core

building

blocks

VTAPrefrontalcortexAccumbens

dopamine

Universal reward systems

sportsmusicdancecrafts arttoolmaking sexfood Dopamine,

Ghrelin,

Leptin,…

Universal reward systems

sportsmusicdancecrafts arttoolmaking sexfood

PFC

CG

OFC

NAcc

Amyg

STR

TH PIT

HIP

SN

VTA dopamine

Reward

Drive

Control

Memory

Robust and evolvable

VTA dopamine

Universal reward systemssportsmusicdancecrafts arttoolmaking sex

food

Glucose

Oxygen

Organs

Tissues

Cells

Molecules


Blood

Constraints

that

deconstrain

Reward Drive Control Memory

dopamine

Blood

Glucose

Oxygen

Constraints

Modularity 2.0

sportsmusicdancecrafts arttoolmaking sex

food

Organs

Tissues

Cells

Molecules


that

deconstrain

Modularity 2.0

Layered architectures

“bow-tie”

diverse diverse

dopamine

workfamily communitynature

Universal reward/metabolic systems

Robust and adaptive, yet …

foodsextoolmakingsportsmusicdancecrafts art

Blood Organs

Tissues

Cells

Molecules




Organs

Tissues

Cells

Molecules


Modularity 1.0

“Weak linkage”

dopamine

Most important “modules”

Blood

Not weakly connected to others,

but highly connected

Modularity 2.0

dopamine


Universal reward/metabolic systems

Robust and adaptive, yet …


Blood Organs

Tissues

Cells

Molecules


sexfoodtoolmakingsportsmusicdancecrafts art


cocaineamphetamine

dopamine

Blood Organs

Tissues

Cells

Molecules



sextoolmakingsportsmusicdancecrafts art

dopamine

Vicarious

money

saltsugar/fatnicotinealcohol

industrialagriculture

market/consumerculture

OrgansTissuesCellsMolecules


Vicarious

money


high sodium

obesity

overwork

smoking

alcoholism

drug abuse

hyper-tension

athero-sclerosis

diabetes

inflammation

immunesuppression

coronary,cerebro-vascular,reno-vascular

cancer

cirrhosis

accidents/homicide/suicide

From Sterling

VTA

Prefrontalcortex

Accumbens

dopamine

Universal reward systemssportsmusicdancecrafts arttoolmaking sexfood

Glucose

Oxygen

Organs

Tissues

Cells

Molecules


Blood

food

VTA dopamine

Glucose

Oxygen

Vicarious

money


high sodium

obesity

overwork

smoking

alcoholism

drug abuse

hyper-tension

athero-sclerosis

diabetes

inflammation

immunesuppression

coronary,cerebro-vascular,reno-vascular

cancer

cirrhosis

accidents/homicide/suicide

Yet Fragile

Robust Yet Fragile

Human complexity

Metabolism


Microbe symbionts

Immune/inflammation

Neuro-endocrine

Complex societies

Advanced technologies

Risk “management”

Obesity, diabetes

Cancer

Parasites, infection

AutoImmune/Inflame

Addiction, psychosis…

Epidemics, war…

Disasters, global &!%$#

Obfuscate, amplify,…


Robust Fragile Metabolism



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: New robust/fragile “conservation laws”


Both

Chandra, Buzi, and Doyle

Yet Fragile

Fix?

Obesity, diabetes

Cancer


AutoImmune/Inflame


Epidemics, war…

Catastrophes


“Architecture”

Don’t worry ...

• “There’s an app for that.”

• “The rapture is near.”

• “There’s a gene…

• “The market will...”

• “The new sciences of …”

• “Order for free..”

• “Like, dude, like, chill…”

In the real (vs virtual) world

What matters:

• Action

What doesn’t:

• Data

• Information

• Computation

• Learning

• Decision

• …

sense

move Spine

sense

move Spine

Reflex

Reflect

Yet Fragile

Fix?

Obesity, diabetes

Cancer


AutoImmune/Inflame


Epidemics, war…

Catastrophes



What matters:

• Action

• Automation

• Limits

• Tradeoffs

wasteful?

fragile?

slow?

?

What we want to avoid.

IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS,

JULY 2010, Alderson and Doyle

robust

efficient wasteful

fragile

Want robust

and efficient

wasteful

fragile

robust

efficient

At best we

get one

wasteful

fragile

robust

efficient

Often

neither

???

Bad

theory?

???

?

?

Bad

architectures?

wasteful

fragile

gap?

robust

efficient

Control Comms

Compute Physics

ShannonBode

Turing

Godel

Einstein

Heisenberg

Carnot

Boltzmann

Theory?Deep, but fragmented, incoherent, incomplete

Control Comms

Compute Physics

ShannonBode

Turing

Godel

Einstein

Heisenberg

Carnot

Boltzmann

wasteful?

fragile?

slow?

?

• Each theory one dimension

• Important tradeoffs acrossdimensions

• Progress is encouraging but…

Case studies

wasteful

fragile

Sharpen

hard bounds

Conservation

“laws”?

Case studies

wasteful

fragile

Sharpen

hard bounds

bad Find and

fix bugs

Case studies

wasteful

fragile

Sharpen

hard bounds

bad Find and

fix bugsBad

architectures?

J. Fluid Mech (2010)

Streamlined

Laminar Flow

Turbulent Flow

Turbulence and drag?

Streamlined

Laminar Flow

Turbulent Flow

Turbulence and drag?

uz

x

yFlow

Coherent structures

wU

z x

y

wU

z x

y

Blunted turbulent velocity profile

Laminar

Turbulent

wU

0u

1uu u p u

t R

“turbulence is a highly nonlinear

phenomena”

0u

1uu u p u

t R

Small Large

Robust Simple Organized

Fragile chaocritical Irreducibile

Complexity?

0u

1uu u p u

t R

Small Large

RobustSimple

2d, linear

Organized

Computer

Fragilechaocritical

3d, nonlinearIrreducibile?

Complexity?

mildlynonlinear

highly nonlinear

Physics of Fluids (2011)

wU

z x

y

uz

x

yFlow

upflowhigh-speed

region

downflowlow speed

streak


Laminar

Turbulent

wU3D coupling

Coherent structures and turbulent drag

Case studies

wasteful

fragile

Sharpen

hard bounds

bad

Find and

fix bugs

Bad

architectures?

Case studies

wasteful

fragile

Sharpen

hard bounds

Theory + biology case study

Chandra, Buzi, and Doyle

Theory + biology case study

• Universal issues

• Longstanding mystery (century? millennia?)

• Accessible, components “well-known”

• Evolution + physiology + “CDS/CME”

• Broadly relevant

• Science paper in press (w/ Fiona Chandra, Genti Buzi)

• Extreme responses typical

Glycolytic oscillations

Hard tradeoffs between

1. Fragility (disturbance rejection)

2. Amount (of enzymes)

3. Complexity (of enzymes)

• Most ubiquitous/studied “circuit” in science/engineering

• New insights and experiments

Metabolic overhead

Fragility

simple

Metabolic overhead

complex

Hard tradeoffs between

1. Fragility (disturbance rejection)

2. Amount (of enzymes)

3. Complexity (of enzymes)

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

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.

0 20 40 60 80 100 120 140 160 180 2000

1

2

3

4

v=0.03

0 20 40 60 80 100 120 140 160 180 2000

0.5

1

1.5

2

v=0.1

0 20 40 60 80 100 120 140 160 180 2000.2

0.4

0.6

0.8

1

v=0.2

“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.

0 20 40 60 80 100 120 140 160 180 2000

1

2

3

4

v=0.03

0 20 40 60 80 100 120 140 160 180 2000

0.5

1

1.5

2

v=0.1

0 20 40 60 80 100 120 140 160 180 2000.2

0.4

0.6

0.8

1

v=0.2

SimulationExperiments

Why?

simple enzyme

Fragility

Enzyme amount

complex enzyme

lnz p

z p

2 2

0

1ln ln

z z pS j d

z z p

Theorem

z and p functions of enzyme complexity

and amount

L

u

x

qm

M

m

M

Linearized pendulum

on a cart

TCA

Gly

G1P

G6P

F6P

F1-6BP

PEP Pyr

Gly3p

13BPG

3PG

2PG

ATP

NADH

Oxa

Cit

ACA

Autocatalysis

Control

• Autocatalytic • Complex vssimple enzymes

Feedbacks

Translation

L

m

M

• Up• eyes vsno eyes

0

1ln 0S j d

Easy, even with eyes closed

No matter what the length

0

ln 0

Gratuitous fragility

Fragile robustness

S j d

Gratuitous fragility

versus

fragile robustness

0

1ln S j d p

1 1g

p z r rL

small largep L

Up is hard for shorter lengths

Down easy, even with

• eyes closed

• all lengths

Fragility

complex

This is a cartoon,

but can be made

precise.

L

0

1ln S j d p

1p

L

L

Too

fragile Why oscillations?

Side effects of

hard tradeoffs

L

1

1 1

1 1

g mz p z r r

L M

p z r

p z r

m

M

Eyes closed

2 2

0

1ln ln

z z pS j d

z z p

Want r and z large (but p small).

Fragility

up + eyes

lnz p

z p

2 2

0

1ln ln

z z pS j d

z z p

Theorem

up, no eyes

This is a cartoon, but can be made

precise.

L

hopeless

down

TCA

Gly

G1P

G6P

F6P

F1-6BP

PEP Pyr

Gly3p

13BPG

3PG

2PG

ATP

NADH

Oxa

Cit

ACA

Autocatalysis

ControlL

m

M


Feedbacks

Translation

• Up• eyes vsno eyes

Fragility Different “architectures”

up-eyes


precise.

Ldown

2 2

0

1ln ln

z z pS j d

z z p

Theorem

lnz p

z p

Fragility Different “architectures”

up-eyes


precise.

Ldown

2 2

0

1ln ln

z z pS j d

z z p

Theorem

complex enzyme

lnz p

z p

simple enzyme

Fragility

up-eyes


precise.

L

2 2

0

1ln ln

z z pS j d

z z p

Theorem

simple enzyme

lnz p

z p

Too fragile

Fragility

Enzyme amount

complex enzyme

lnz p

z p

2 2

0

1ln ln

z z pS j d

z z p

Theorem

Why oscillations?Side effects of hard tradeoffs

k

z p

z p

10-1

100

10110

0

101

Fragility Biological architectures

achieve hard limits and

use complex enzymes

and networks

2 2

0

1ln ln

z z pS j d

z z p

complex enzyme

Enzyme amount

• Microfluidic experiments

• Yeast strain W303 grown in Ethanol

• Glucose and KCN added anaerobic glycolysis

• NADH measured every 3 s

0 60 120 180 240 3000

50

100

150

t ( seconds)

NADH

Architecture Good architectures

allow for effective

tradeoffs

wasteful

fragile

2 2

0

1ln ln

z z pS j d

z z p

Theorem

• z and p are functions of enzyme complexity and amount• standard biochemistry models• phenomenological

• first principles?

What reviewers say• “If such oscillations are indeed optimal, why are they not

universally present?” • “The approach to establish universality for all biological and

physiological systems is simply wrong. It cannot be done…” • “While the notion of universality is well justified in physics, it is

perhaps not so useful in biological sciences and medicine. To develop a set of universal principles for biological and physiological systems is mostly likely a dream that will never be realized, due to the vast diversity in such systems.”

• “…does not seem to have an understanding or appreciation of the vast diversity of biological and physiological systems…”

• “…desire to develop rigorous framework is understandable, but usually this can be done only by imposing a high degree of abstraction, which would then make the model useless …”

• “… a mathematical scheme without any real connections to biological or medical problems…”

Fragilityhard limits

simple

Overhead, waste

complex

• General• Rigorous• First principle

• Domain specific• Ad hoc• Phenomenological

Plugging in domain details

?

Control Comms

Physics

Wiener

Bode

Kalman

Heisenberg

Carnot

Boltzmann

robust control

• Fundamental multiscale physics• Foundations, origins of

– noise – dissipation– amplification

• General• Rigorous• First principle

?

IEEE TRANS ON AUTOMATIC CONTROL,

FEBRUARY, 2011

Sandberg, Delvenne, and Doyle

http://arxiv.org/abs/1009.2830

Gen

es

Co-factors

Pro

teins

Pre

curs

ors

Autocatalytic feedback

Nu

trie

nts

Core metabolism

DNA

replication

Trans*

Cat

aboli

sm

Carriers

Environment Environment

Huge

Variety

Huge

Variety

Bacterial cell

TCA

Gly

G1P

G6P

F6P

F1-6BP

PEP Pyr

Gly3p

13BPG

3PG

2PG

ATP

NADH

Oxa

Cit

ACA

Autocatalysis

Control


Feedbacks

Catabolism

TCAPyr

Oxa

Cit

ACA

Gly

G1P

G6P

F6P

F1-6BP

PEP

Gly3p

13BPG

3PG

2PG

ATP

NADH

Cat

aboli

sm

AA

Ribosome

transl. Proteins

Pre

curs

ors

ATP

ATP

Inside every cell

Ribosomes

make

ribosomes

Translation: Amino acids

polymerized into proteins

Cat

aboli

sm

AA

Ribosome

RNARNAp

transl. Proteins

xRNAtransc.

Pre

curs

ors

DNADNAp

Repl. Gene

ATP

ATP

Building

Blocks

• Translation

• Transcription

• DNA Replication

Cat

aboli

sm

AA

Ribosome

RNARNAp

transl. Proteins

xRNAtransc.

Pre

curs

ors

DNADNAp

Repl. Gene

ATP

ATP

Enzymes

Building

Blocks

Crosslayer

autocatalysis

Macro-layers

Inside every cell

NucleotidesCatabolism

Pre

curs

ors

Taxis and

transport

Nu

trie

nts

Carriers

Core metabolism

Same

12

in all

cells

Same

8

in all

cells

Gen

es

Co-factors

Polymerization and complex

assemblyP

rotein

sP

recu

rsors

Autocatalytic feedback

Taxis and

transport

Nu

trie

nts

Core metabolism

DNA

replication

Trans*

Cat

aboli

sm

Carriers

100 104 to ∞in one

organisms

Huge

Variety

12

8

Other examples

Clothing

Lego

Money

Cell biology

Shirt

Slacks

Jacket Tie

T-Shirt

Socks

ShoesCoatShorts

Soft layering

Wool

Cotton

Nylon

Silk

Polyester

Rayon

Shirt

Slacks

Jacket Tie

T-Shirt

Socks

ShoesCoatShorts

Modularity?

Shirt

Slacks

Jacket Tie

T-Shirt

Socks

ShoesCoatShorts

Given a wardrobe (set of garments)

1 << # outfits << # non-outfits

(random heaps are of garments are never outfits)

Shirt

Slacks

Jacket Tie

T-Shirt

Socks

ShoesCoatShorts

1 << # outfits << # non-outfits

(random heaps are of garments are never outfits)

large thin

large thin

1 << # toys << # non-toys

“order for free”

Letters and words

• 9 letters: adeginorz

• 9!= 362,880 sequences of 9 letters

• Only “organized” is a word

1 << (# words) << (# non-words)

large thin

Shirt

Slacks

Jacket sweater

T-Shirt

Socks

ShoesCoatShorts

Inne

r

Mid

dle

Oute

r

Skin

Hidden

Robust to variations in

• weather

• activity

• appearance requirements

• wear and tear

• cleaning

System

constraints

Wool Cotton NylonSilk Polyester Rayon

Cloth

Shirt

Slacks

Jacket Tie

T-Shirt

Socks

ShoesCoatShorts

Sewing

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Modularity 2.0

Prevents unraveling of lower layers

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Hidden,

large, thin,

nonconvex

Outfits

Assemble

Wool

Cotton

Nylon

Silk

Polyester

Rayon

Shirt

Slacks

Jacket Ti

e

T-Shirt

Socks

ShoesCoatBoxers

The

hourglass?

Dress Shirt Slacks Lingerie Coat Scarf Tie

Garments

Cloth

Sewing

Wool Cotton NylonSilk Polyester

Material technologies

Rayon

Horizontal networks of fibers

Ver

tica

l d

ecom

posi

tion

Horizontal networks of garments

Ver

tica

l d

ecom

posi

tion

Inne

r

Mid

dle

Ou

ter

Horizontal networks of fibers

Horizontal networks of garments

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Universal strategies?

Garments have

limited access to

threads and fibers

constraints on

cross-layer

interactions

quantization

for robustness

Even though

garments seem

analog/continuous

Prevents unraveling of lower layers

Fiber

Geographically diverse sources

Diverse fabric

Functionally diverse garments

General

purpose

machines Diverse Thread

sew

knit, weave

spin

Functionally diverse garments

Cloth

Thread

Fiber

Garments

Fragilities?

Cloth

Thread

Fiber

Garments

Scalable

Sustainable?

Embedded

virtual

actuator/

sensor

Network

cable

Controller

Lib

App

DIF

Networked/embedded/layered

Lib

Physical

plant

Embedded

virtual

actuator/

sensor

Network

cable

Controller

DIF

Physical

plant

Meta-layering of cyber-phys control

Physiology

Organs

Cells


Physiology

Organs


Cells

Physiology

Organs

Meta-layers

Prediction

Goals

Actions

errors

ActionsCo

rte

x

Which blue line is longer?

Meta-layers

Fast,

Limited

scope

Slow,

Broad

scope

Unfortunately, we’re not

sure how this all works.

Shirt

Slacks

Jacket sweater

T-Shirt

Socks

ShoesCoatShorts

Inne

r

Mid

dle

Oute

r

Skin

Selection

assembly

Cloth

Thread

Fiber

Garments

Xform Ctrl Mgmt

Networked,

universal,

layeredXform Ctrl Mgmt

Xform Ctrl Mgmt

Xform Ctrl Mgmt

Do

ma

in s

pe

cific

Cloth

Thread

Fiber

Garments

Xform Ctrl Mgmt

Networked,

universal,


Xform Ctrl Mgmt

Xform Ctrl Mgmt

Dem

an

d

Su

pp

ly

Cloth

Thread

Fiber

Garments

Xform Ctrl Mgmt

Networked,

universal,


Xform Ctrl Mgmt

Xform Ctrl Mgmt

Co

ntro

l

Su

pp

ly

Complexity?

Yet Fragile

Fix?

Obesity, diabetes

Cancer


AutoImmune/Inflame


Epidemics, war…

Catastrophes



What matters:

• Action

• Automation

• Limits

• Tradeoffs

THE END OF THEORYScientists have always relied on hypothesis and experimentation. Now, in the era of

massive data, there’s a better way.

“All models are wrong, and increasingly

you can succeed without them.”

Save our

children

Peta-

philia

There is a

treatment.

New words

• Peta-philia: Perverse love

of data and computation

• Peta-fop: Someone who

profits from peta-philia

• Exa-duhs: Loss of clue

from excessive peta-philia

Fortunately

there seems

to be a

treatment

Not yet in

widespread use

Peta-

philia

Case studies

wasteful

fragile

Sharpen

hard bounds

bad

Find and

fix bugs

wasteful

fragile

bad

Find and

fix bugs

App AppApplications

Router

Chiang, Low, Calderbank, and Doyle

TCPIP

Physical

MAC

Switch

MAC MAC

Pt to Pt Pt to Pt

Diverse applications


Control

Compute

Internet

optimization

operating systems

“Layering as optimization”

robust control

• 10+ years of progress & impact…

• Static optimization dynamic control

• Wireless, scheduling, net coding, …

• But….

• Something is wrong architecturally

• Better protocols/control won’t fix it

• Design: from protocols to architectures

Layered architectures everywhere

• Computers, Internet, software… cyber-physical?

• Bacterial biosphere

• Evo-devo

• Brain

• Lego, clothing, supply chain, …

• Useful “comparative physiology”

TCPIP

Physical



Diverse

OS

Physical



• OS allocates/shares

– diverse resources among

– diverse applications

• “Strict layering” crucial,

e.g. clearly separate

– Application name space

– Logical (virtual)

name/address space

– Physical (name/) address

space

• Name resolution w/in appls

• Name/addr transl X layers

OS

Physical



In operating systems:

Don’t cross layers

(rings)

Direct

access to

physical

memory?

In programming:

No global variables

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?

App AppApplications

Router

IP addresses

interfaces

(not nodes)

App App

IPC

Global

and direct

access to

physical

address!

Robust?

• Secure

• Scalable

• Verifiable

• Evolvable

• Maintainable

• Designable

• …

DNS

IP addresses

interfaces

(not nodes)

OS

Diverse hardware


Little diversity


OS

Diverse hardware


“Hourglass”


Physical

IP

TCP

Application

Naming and addressing need to be

• resolved within layer

• translated between layers

• not exposed outside of layer

Related “issues”

• DNS

• NATS

• Firewalls

• Multihoming

• Mobility

• Routing table size

• Overlays

• …

Architecture

is not graph

topology.

Architecture

facilitates

arbitrary graphs.

Persistent

errors and

confusion.

Notices of the AMS, 2009

wasteful

fragile

slowGood case studies

bad

worse

“New sciences” of “complexity” and “networks”?

“New sciences” of “complexity” and “networks”? worse

• Edge of chaos• Self-organized criticality• Scale-free “networks”• Creation “science”• Intelligent design• Financial engineering• Risk management• “Merchants of doubt”• …

• Science as pure fashion• Ideological• Political• Evangelical• Nontech trumps tech

D. Alderson, NPS 178

Complexity = hard to understand, explain

• “Complexity science” = persistently in error

– bionetworks: gene regulation, metabolism, PPI

– wildfires, earthquakes

– Internet, power grid

• Minimal impact on technology

• Diminishing impact on biology

• Of concern in medicine, neuroscience

Complex systems?

Fragile

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

Even small

amounts can

create

bewildering

complexity

Complex systems?

Fragile

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

Robust

Complex systems?

• Resources

• Controlled

• Organized

• Structured

• Extreme

• Architected

• …

Robust complexity

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

Architecture

• Resources

• Controlled

• Organized

• Structured

• Extreme

• Architected

• …

Robust complexity

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

New

wordsFragile complexity

Emergulent

Emergulence

at the edge of

chaocritiplexity

• Scale

• Dynamics

• Nonlinearity

• Nonequlibrium

• Open

• Feedback

• Adaptation

• Intractability

• Emergence

• …

wU

z x

y


Laminar

Turbulent

wU

0u

1uu u p u

t R

“turbulence is a highly nonlinear

phenomena”

0u

1uu u p u

t R

Small Large

RobustSimple

2d, linear

Organized

Computer

Fragilechaocritical

3d, nonlinearIrreducibile?

Complexity?

mildlynonlinear

highly nonlinear

Irreducibility and “intelligent design”

Rube Goldberg

The essential

ID argument

If biology is like this,

then it could not have evolved.

• This is actually true, and in fact…

• If biology is like this,

• then it would be too fragile to persist,

• and would need the constant intervention of

supernatural forces

The flaw

• It is too fragile to actually build.

• Neither biology nor (most of) technology is

anything like this.

• Who said otherwise? Lots of real scientists!

• Oops! (But we are too fragile and unsustainable.)

This is a

cartoon.

john doyle john g braun professor control and dynamical...

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