The Internet hourglass

1
The Internet hourglass
IP
2
Network protocols.
Files
HTTP
TCP
IP
packets
packets
packets
packets
packets
packets
Routing Provisioning
3
Network protocols.
HTTP
TCP
Vertical decomposition Protocol Stack
IP
Routing Provisioning
4
Network protocols.
HTTP
TCP
IP
Horizontal decomposition Each level is
decentralized and asynchronous
Routing Provisioning
5

• Breaks standard communications and control
  theories.
• Coherent, complete theory is missing but
  possible. First cut nearly done.
• What about future challenges of embedded,
  everywhere?

HTTP
TCP
Vertical decomposition
IP
Horizontal decomposition
Routing Provisioning
6
Two great abstractions

• Separate systems
• from physical substrate
• into control, communications, and computing
• Facilitated massive, wildly successful, and
  explosive growth in both mathematical theory and
  technology
• but creating a new Tower of Babel where even the
  experts do not read papers or understand systems
  outside their subspecialty.

7
Biology and advanced technology

• Biology
• Integrates control, communications, computing
• Into distributed control systems
• Built at the molecular level
• Advanced technologies will do the same
• We need new theory and math, plus unprecedented
  connection between systems and devices
• Two challenges for greater integration
• Unified theory of systems
• Multiscale from devices to systems

8
Bonus!

• The new mathematics for a unified theory of
  systems is directly relevant to multiscale
  physics, and vice versa. New approaches and
  solutions to
• Ubiquity of power laws (statistical mechanics)
• Shear flow turbulence (fluid dynamics)
• Complexity and phase transitions
• Macro dissipation and thermodynamics from micro
  reversible dynamics (statistical mechanics)
• Quantum entanglement (see recent PRL paper by
  Parrilo)
• Quantum-classical transition and measurement
• The two challenges are connected.

9
Biochemical Network E. Coli Metabolism
From Adam Arkin
from EcoCYC by Peter Karp
10
Nutrients, ions, gases,
Complex Carbohydrates
Nucleotides
Complex Lipids
Nucleotides
Carbohydrates
Carbohydrates
Lipids
Lipids
Amino Acids
Amino Acids
Energy
Energy
www.genome.ad.jp/kegg
11
Biochemical Network E. Coli Metabolism
Enzyme
Metabolite
From Adam Arkin
from EcoCYC by Peter Karp
12
Biochemical Network E. Coli Metabolism
Regulatory Interactions
Complexity ? Fragility
Supplies Materials Energy
Supplies Robustness
From Adam Arkin
from EcoCYC by Peter Karp
13
Biological network mechanisms

• Negative feedback for regulation
• Feedforward control
• Protocol stacks and modularity
• Positive feedback to create switches and memory
  (digital systems)
• Autocatalysis (positive feedback in energy and
  materials)
• Oscillators for timing
• Randomized strategies
• Coding

14
Biochemical Network E. Coli Metabolism

• Constraints
• Mass and energy balances

(e.g. Palsson,)
from EcoCYC by Peter Karp
15
Engineering systems have just reached biological
levels of
16
Coal
waste
electricity
17
Coal
waste
electricity
18
Electricity generation and consumption
http//phe.rockefeller.edu/Daedalus/Elektron/
19
log(complexity)
time
20
Biological optimality?

• In what sense are organisms optimal?
• Is biological organization relevant to
  engineering, particularly embedded networking?
• Much has been made recently about allometric
  scaling
• Briefly review this, as a starting point in
  discussing relationship between biology,
  engineering, convergent evolution, and optimality

21
Metabolic rate

• Data suggests

• Various theories suggest slightly different
  scaling
• Optimal load bearing ? 2/3.66
• Optimal fractal nutrient distribution networks ?
  3/4.75
• Data inconclusive about which is better
• Both are probably relevant, optimality is
  combination of many factors
• Much literature recently (e.g. Enquist, et al)

22
"If Galileo were a biologist, he would have
written a big fat tome on the details of how
different objects fall at different rates.
Geoffrey West Santa Fe Institute   Theoretical
Division, Los Alamos National Laboratory
"as potentially important to biology as Newton's
contributions are to physics K. J. Niklas
23
Physiology The pitfalls of power laws
EWALD R. WEIBEL
        09 May 2002
                                              
                                                
                
Nature 417, 131 - 132 (2002)
24
Allometric cascade as a unifying principle of
body mass effects on metabolism
CHARLES-A. DARVEAU, RAUL K. SUAREZ,
RUSSEL D. ANDREWS PETER W. HOCHACHKA
Until now, the classical approach to the basal
metabolic rate (BMR) allometry problem has been
to search for the single driving force or single
rate-limiting step enforcing its scaling
behaviour on overall metabolism. However, this
concept in metabolic regulation studies was
abandoned during the 1960s and was replaced by
the concept of multiple control sites in
metabolic pathways.
25
Boeing 747
F-16
Cruising speed (m/s)
bee
crane fly
fruit fly
damsel fly
Compare with cruise speed for flight at sea level
(from Tennekes)
Mass (grams)
26
Boeing 747
F-16
Beech Baron
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly
Cruise speed at sea level (from Tennekes)
Mass (grams)
27
Boeing 747
F-16
Beech Baron
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly
Cruise speed at sea level (from Tennekes)
Mass (grams)
28
L
AArea
W
29
Boeing 747
F-16
Beech Baron
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly
-3
3
9
10
10
10
Mass (grams)
Simple explanation
30
Variations from nominal
Boeing 747
Short wings, maneuverable
F-16
Beech Baron
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly
9
Long wings, soaring and gliding
-3
Not optimized for flight
10
10
Mass (grams)
31
Boeing 777

• Aeronome 150,000 different components
• (for a total of 3,000,000),
• some with millions of subparts.

During flight test, a partial system state is
saved at the rate of 1e8 bits (100 Mbits) per
second.
The human genome can be stored with 1e10 bits (lt
2 CDs).
32
Boeing 777 and redundancy

• Aeronome 150,000 different components
  (little redundancy)
• For a total of 3,000,000, mostly rivets (lots of
  redundancy)
• but some with millions of subparts.
• Redundancy is the most primitive and most common
  mechanism for robustness, but contributes little
  to complexity.

33
In the atmosphere
Boeing 747
F-16
Beech Baron
Boeing 777
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly
Mass (grams)
Cruise speed at sea level (from Tennekes)
34
In a wind tunnel
Boeing 747
F-16
Beech Baron
Boeing 777
Cruising speed (m/s)
goose
sailplane
starling
eagle
hummingbird
house wren
bee
Human
crane fly
fruit fly
dragonfly
damsel fly

• Imagine an extreme mutant 777
• Knock out gt90 of its aeronome
• No phenotype!

Mass (grams)
Cruise speed at sea level (from Tennekes)
35
Boeing 777

• Aeronome 150,000 different components .

• Mostly control systems, plus associated
  sensors, actuators, computers, and networks.
• Nonessential in an ideal laboratory
  environment.
• Provides robustness, but not basic
  functionality.
• Complexity is dominated by robustness.

36
Allometric scalings

• Derivable using simple laws, assuming optimal
  design.
• Variations from exact scaling as interesting as
  the ideal fit.
• Rarely is biology optimized in these simple
  senses.
• Says little about biological complexity, and not
  much about aircraft design, but
• illustrates how thinking about optimal design
  can give a starting point for comparative
  studies, among organisms and with engineering.

37
Important message Highly structured, optimized,
robust, nongeneric configurations
38
(No Transcript)
39
essential 230   nonessential 2373  
unknown 1804   total 4407
http//www.shigen.nig.ac.jp/ecoli/pec
40
Biochemical Network E. Coli Metabolism
From Adam Arkin
from EcoCYC by Peter Karp
41
Nutrients, ions, gases,
Complex Carbohydrates
Nucleotides
Complex Lipids
Nucleotides
Carbohydrates
Carbohydrates
Lipids
Lipids
Amino Acids
Amino Acids
Energy
Energy
www.genome.ad.jp/kegg
42
Biochemical Network E. Coli Metabolism
Enzyme
Metabolite
From Adam Arkin
from EcoCYC by Peter Karp
43
Biochemical Network E. Coli Metabolism
Regulatory Interactions
Complexity ? Fragility
Supplies Materials Energy
Supplies Robustness
From Adam Arkin
from EcoCYC by Peter Karp
44
Biological network mechanisms

• Negative feedback for regulation
• Feedforward control
• Protocol stacks and modularity
• Positive feedback to create switches and memory
  (digital systems)
• Autocatalysis (positive feedback in energy and
  materials)
• Oscillators for timing
• Randomized strategies
• Coding

45
Biochemical Network E. Coli Metabolism

• Constraints
• Mass and energy balances

(e.g. Palsson,)
from EcoCYC by Peter Karp
46
Metabolism Constraints Mass and Energy
balance (Note At the molecular level this
implies a huge number of constraints.)
Nutrients, ions, gases,
Nucleotides
Carbohydrates
Lipids
Amino Acids
Energy
47
Nutrients, ions, gases,
Is this complexity needed? Is this network
optimal? If so, in what sense?
48
Biochemical Network E. Coli Metabolism
Regulatory Interactions
Constraints?
Supplies Robustness
Fragility is constrained.
From Adam Arkin
from EcoCYC by Peter Karp
49
Process
DNA replication
DNA
proteins
Transcription/ Translation
Central dogma
50
Process
Products
Substrates
Enzymes
Metabolism
Trans
DNArep
51
Process
Products
Substrates
Enzymes
52
Controls
Measurements
Process
Products
Substrates
Enzymes
53
Environment
Transport
Control
Metab.
Trans
DNArep
Cell Cycle
54
Transport
Environment
Metabolism
Trans
DNArep
Cell Cycle
55
Process
Products
Substrates
Megabolism?
Enzymes
Environment
Transport.
Trans
Metab.
DNArep
C.C.
Metabolism
56
Environment
Metabolism
57
Minimal organisms
Environment
and the parts of complex cells which are
essential for laboratory viability
Control
Metabolism
58
essential 230   nonessential 2373  
unknown 1804   total 4407
http//www.shigen.nig.ac.jp/ecoli/pec
59
The Environment Is Uncertain
Metabolism
Complex cells
60
Environment
A-life
Metabolism
61
Environment
 charf"charfcscmain()   printf(f,34,f,34,
10)c"      main()printf(f,34,f,34,10)
Self reproducing automata?
62

• Metabolism, cell cycle, and transport are built
  on top of biochemistry
• Constrained by laws of biochemistry and its
  Transcription/Translation and DNA replication
• A minimal cell requires a few hundred genes
  (the genetic module part count) and a steady,
  nutrient rich environment

Environment
Control
Essential

• Autocatalysis (positive feedback in energy and
  materials)
• Oscillators for timing cell cycle and metabolism

Metabolism
63
Environment
Metabolism
Complex organisms
64
Toy metabolism
X0
X1
Xk
Xn


Error
perturbation
65


-
(higher order dynamics)
delay
66


-
X0
X1
Xi
Xn


Error
perturbation
67
1.05
Step increase in demand for ATP.
ATP
1
h 3
0.95
h 2
0.9
h 1
0.85
h 0
0.8
0
5
10
15
20
Time (minutes)
h 0 1 2 3
68
1.05
1
h 3
0.95
h 2
ATP
0.9
h 1
0.85
h 0
0.8
0
5
10
15
20
h 0 1 2 3
Time (minutes)
0.8
h 3
0.6
0.4
h 2
0.2
h 0
Log(Sn/S0)
0
h 1
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
69
1.05
Ideal response
ATP
1
h 3
0.95
Time response
0.9
0.85
h 0
0.8
0
5
10
15
20
Time (minutes)
0.8
h 3
0.6
Spectrum
0.4
Normalized logarithm of spectrum of error.
0.2
h 0
Log(Sn/S0)
0
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
70
1.05
Ideal response
ATP
1
h 3
0.95
Time response
0.9
0.85
h 0
0.8
0
5
10
15
20
Time (minutes)
0.8
h 3
0.6
Spectrum
0.4
Normalized spectrum of error.
0.2
h 0
Log(Sn/S0)
0
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
71
h 3
h 2
h 1
h 0
Time
0
5
10
15
20
0.8
h 3
0.6
0.4
h 2
0.2
h 0
Log(Sn/S0)
0
h 1
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
72
Transients, Oscillations
0.8
h 3
0.6
Tighter steady-state regulation
0.4
h 2
0.2
h 0
Log(Sn/S0)
0
h 1
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
73
Transients, Oscillations
logS
?
Biological complexity is dominated by the
evolution of mechanisms to more finely tune this
robustness/fragility tradeoff.
Tighter regulation
74
h 3
h 2
h 1
h 0
Time
0
5
10
15
20
0.8
h 3
0.6
0.4
h 2
0.2
h 0
Log(Sn/S0)
0
h 1
-0.2
-0.4
-0.6
-0.8
0
2
4
6
8
10
Frequency
75
h 3
h 2
h 1
Transients, Oscillations
h 0
Time
0
5
10
15
20
Tighter steady-state regulation
Frequency
76
Biochemical Network E. Coli Metabolism
Regulatory Interactions
Complexity ? Fragility
Fragility is constrained.
Supplies Robustness
From Adam Arkin
from EcoCYC by Peter Karp
77
HVAC Heating, Ventilation, and Cooling
Temperature Disturbances
Weather
78
Regulation of HVAC
Disturbances
Weather
HVAC
People
79
Weather
Disturbances
Heat/cool
People
Energy
80
Robust yet fragile
Fragile
With heat/AC
No heat/AC
Fragile
Energy
Robust
Energy supply
Disturbances
Components
Robust
81
Thermostat
Fragility to component malfunction
HVAC
With heat/AC
Fragile
Robust
Components
82
Thermostat
Temperature
Fragility to component malfunction
HVAC
With heat/AC
Fragile
Components
83
Universal tradeoffs in complex networks
Fragile
Energy and Materials
Disturbances and Components
Disturbances and Components
Robust
84
For some property of a system or model
Computation Experimentation Understanding
Complexity is large cost for
Note Both require a specific property.
85
Greater complexity
Fragile
Energy and Materials
Robust
86

• In development
• drive-by-wire
• steering/traction control
• collision avoidance

87
Cascading events in car crashes
Normal
Danger
Crash
Contact w/car
Trauma
Barriers in state space
88
Normal
Danger
Crash
Contact w/car
Trauma
Normal
Sense/ Deploy
Contact w/bag
Trauma
89
Full state space
Desired
Worse
Bad
90
Full state space
Robust
Yet Fragile
91
Robust, yet fragile

• Robust to uncertainties
• that are common,
• the system was designed for, or
• has evolved to handle,
• yet fragile otherwise
• This is the most important feature of complex
  systems (the essence of HOT).

92
Humans supply most feedback control
Normal
Danger
Crash
Contact w/car
Trauma
Lanes Laws Lights Ramps
Collision avoidance Anti-lock brakes
Heavy metal Seat belts Airbags
Helmets
93
Fully automated systems?
Normal
Danger
Crash
Contact w/car
Trauma
Lanes Laws Lights Ramps
Collision avoidance

• Internally unimaginably more complex.
• Superficially much simpler?
• Biological systems are fully automated

94
Robustness of HOT systems
Fragile
Humans
Chess
Meteors
Robust
95
Robustness is a conserved quantity
Fragile
Chess
Meteors
Robust
96
Robustness of HOT systems
Fragile
Humans
Archaea
Chess
Meteors
Machines
Robust
97
Diseases of complexity
Fragile

• Parasites
• Cancer
• Epidemics
• Auto-immune disease

Complex development Regeneration/renewal Complex
societies Immune response
Uncertainty
Robust
98
logS
?
We have a proof of this.
X0
X1
Xi
Xn


Error
Xn1
99
We have no proof of this. Yet.
Fragile

• Parasites
• Cancer
• Epidemics
• Auto-immune disease

Complex development Regeneration/renewal Complex
societies Immune response
Uncertainty
Robust
100
Modeling complex systems
May need great detail here
Fragile
And much less detail here.
Uncertainty
Robust
101
Fragile
Robust (fragile) to perturbations in components
and environment ? Robust (fragile) to errors and
simplifications in modeling
More detail.
Required model complexity
Less detail.
Uncertainty
Robust
102

• Complexity of system evolution is driven by
  fragilities
• Complexity of experiments, modeling, and
  inference are also driven by fragilities

Fragile
More detail.
Required model complexity
Less detail.
Uncertainty
Robust
103
Biological network mechanisms

• Negative feedback for regulation
• Feedforward control
• Protocol stacks and modularity
• Positive feedback to create switches and memory
  (digital systems)
• Autocatalysis (positive feedback in energy and
  materials)
• Oscillators for timing
• Randomized strategies
• Coding

104
Network mechanisms

• Negative feedback is both the most powerful and
  most dangerous mechanism for robustness.
• It is everywhere in engineering, but appears
  hidden as long as it works.
• Biology seems to use it even more aggressively,
  but also uses all the other familiar engineering
  strategies.

105
Robustness analysis problems

• Question Can we reverse-engineer large
  biological networks using conventional or
  envisioned modeling and simulation approaches?
• Conventional MuTools, SPICE, HLA, NS, CATIA,
  Pro-E, CFD/DNS/LES/, etc
• Answer Not a chance.
• Why?

106
User interface
Modern computation.
Applications
High-level functionality
Applications
Layers of rules and protocols
OS
Computer
Board
VLSI
Physical implementation
107
User interface
VLSI design
Instructions
Applications
Logic
Applications
Topology
OS
Geometry
Computer
Timing
Board
Fabrication
VLSI
Silicon
108
User interface
VLSI design
Instructions
Applications
Logic
Applications
Topology
OS
Geometry
Computer
Timing
Board
Fabrication
VLSI
Silicon
109
User interface
VLSI design
Instructions
Applications
Logic
Applications
Topology
OS
Geometry

• Suppose we were given complete, detailed circuit
  models for all chips, and
• state-of-the-art analog circuit simulators.
• Would we be able to find the bug in our program?

Computer
Timing
Board
Fabrication
Silicon
VLSI
110
User interface
VLSI design
Instructions
Applications
Logic
Applications
Topology
OS
Geometry
Computer
It would be necessary to reverse engineer the
layers of abstractions and protocols as well.
Timing
Board
Fabrication
VLSI
Silicon
111
More necessity and robustness

• Integral feedback and signal transduction
  (bacterial chemotaxis, G protein) (Yi, Huang,
  Simon)

112
Taylor, Zhulin, Johnson
113
(No Transcript)
114
Bacterial chemotaxis
115
Random walk
Ligand
Motion
Motor
116
Biased random walk
gradient
Ligand
Motion
Motor
Signal Transduction
117
High gain (cooperativity)
ultrasensitivity
References Cluzel, Surette, Leibler
Motor
Ligand
Motion
Signal Transduction
118
Motor
References Cluzel, Surette, Leibler Alon,
Barkai, Bray, Simon, Spiro, Stock, Berg,
Signal Transduction
119
ligand binding
motor
FAST
ATT
-ATT
flagellar
motor
R
CH
3
MCPs
MCPs
SLOW
CW
W
W
P
P
-CH
3
A
A


Y
B

P
Z
ATP
ADP
ATP
P
P
Y
B
i
i
120
Fast (ligand and phosphorylation)
ligand binding
motor
FAST
ATT
-ATT
flagellar
motor
MCPs
MCPs
CW
W
W
P
A
A

Y

P
Z
ATP
ADP
ATP
P
Y
i
121
Short time Yp response
1
Ligand
0
0
1
2
3
4
5
6
Che Yp
Barkai, et al
No methylation
Extend run (more ligand)
0
1
2
3
4
5
6
Time (seconds)
122
Slow (de-) methylation dynamics
R
CH
3
MCPs
MCPs
SLOW
W
W
P
-CH
3
A
A

B

P
ATP
ADP
ATP
P
B
i
123
ligand binding
motor
FAST
ATT
-ATT
flagellar
motor
R
CH
3
MCPs
MCPs
SLOW
CW
W
W
P
P
-CH
3
A
A


Y
B

P
Z
ATP
ADP
ATP
P
P
Y
B
i
i
124
Long time Yp response
5
3
1
0
0
1000
2000
3000
4000
5000
6000
7000
No methylation
B-L
0
1000
2000
3000
4000
5000
6000
7000
Time (seconds)
125
Tumble (less ligand)
Ligand
Extend run (more ligand)
126
Biologists call this perfect adaptation

• Methylation produces perfect adaptation by
  integral feedback.
• Integral feedback is ubiquitous in both
  engineering systems and biological systems.
• Integral feedback is necessary for robust perfect
  adaptation.

127
Perfect adaptation is necessary
ligand
128
Tumbling bias
Perfect adaptation is necessary
to keep CheYp in the responsive range of the
motor.
ligand
129
Tumbling bias
130
Ligand

F
ln(S)
F
F ? ?? ln(S) ? ??
extreme robustness
131
Ligand

F
Integral feedback
F ? ?? ln(S) ? ??
132
(No Transcript)
133
HTTP
TCP
Vertical decomposition
IP
Horizontal decomposition
Routing Provisioning
134
E. Coli Heat Shock (with Kurata, El-Samad,
Khammash, Yi)
135
Cell
Temp cell
Temp environ
136
Cell
How does the cell build barriers (in state
space) to stop this cascading failure event?
Temp cell
Temp environ
137
Temp cell
Folded Proteins
Temp environ
138
Temp cell
Folded Proteins
Temp environ
139
More robust ( Temp stable) proteins
Unfolded Proteins
Aggregates
Temp cell
Folded Proteins
Temp environ
140

• Key proteins can have multiple (allelic or
  paralogous) variants
• Allelic variants allow populations to adapt
• Regulated multiple gene loci allow individuals
  to adapt

Unfolded Proteins
Aggregates
Temp cell
Folded Proteins
Temp environ
141
37o
42o
Log of E. Coli Growth Rate
46o
21o
-1/T
142
Robustness/performance tradeoff?
37o
42o
Log of E. Coli Growth Rate
46o
21o
-1/T
143
Heat shock response involves complex feedback and
feedforward control.
Unfolded Proteins
Temp cell
Folded Proteins
Temp environ
144
Alternative strategies
Why does biology (and advanced technology)
overwhelmingly opt for the complex control
systems instead of just robust components?

• Robust proteins
• Temperature stability
• Allelic variants
• Paralogous isozymes
• Regulate temperature
• Thermotax
• Heat shock response
• Up regulate chaperones and proteases
• Refold or degraded denatured proteins

145
Regulation of Heat-Shock Response
146
Mathematical Model
Protein Synthesis
147
Binding Equations
Mass Balance Equations
148
Validation of the model

• FtsH null mutantThe model could reproduce the
  accumulation of s32 in FtsH null mutants (20 fold
  the normal level at low temperature, 30 fold at
  high temperature). The model could also reproduce
  the profile for the HSP rate of induction upon
  heat shock
• Heat shock gene expression following
  overproduction of s32  When the s32 level is
  increased gradually up to 130 fold the normal
  level, the HSP rate of synthesis increases 5 fold
  and then returns to a steady state level 2 fold
  higher than normal after around 20 minutes. The
  model could reproduce this behavior.

149
Regulation of Heat-Shock Response
150
Regulation of Heat-Shock Response
151
3
10
s32
2
10
1
10
6
10
Punfold
4
10
2
10
5
10
DnaK
4
10
3
10
0
20
40
60
80
100
Time (sec)
Log(concentrations)
152
200
150
s32
100
50
0
5
x 10
10
Punfold
5
0
4
x 10
2
DnaK
1.5
1
0.5
0
0
20
40
60
80
100
Time (sec)
153
3
10
200
s32
150
2
100
10
50
1
0
10
5
x 10
6
10
10
4
5
10
Punfold
0
2
10
4
5
x 10
10
2
DnaK
1.5
4
10
1
0.5
3
10
0
0
20
40
60
80
100
0
20
40
60
80
100
Log(concentrations)
Time (sec)
154
Regulation of Heat-Shock Response
Feedforward
155
Punfold
--FF
5
x 10
15
--FF-deg
10
WT
5
0
-deg
8
10
6
10
log
4
10
2
10
400
420
440
460
480
500
156
Punfold
--FF
5
x 10
15
10
WT
5
0
8
10
6
10
log
4
10
2
10
400
420
440
460
480
500
157
12
no FF
10
8
Punfold
6
WT
4
2
2
0
0
s32
1
5
0
WT
1
0
0
5
0
2
0
0
0
0
WT
DnaK
1
6
0
0
0
1
2
0
0
0
8
0
0
0
4
0
0
5
0
0
6
0
0
7
0
0
8
0
0
T
i
m
e
158
Heat
Denaturing
Unfolded proteins
-
159
Regulation of Heat-Shock Response
160
Disturbance
Model
badness
-
161
Predator
trauma
-
162
Alternative strategies
Why does biology (and advanced technology)
overwhelmingly opt for the complex control
systems instead of just robust components?

• Robust proteins
• Temperature stability
• Allelic variants
• Paralogous isozymes
• Regulate temperature
• Thermotax
• Heat shock response
• Up regulate chaperones and proteases
• Refold or degraded denatured proteins

163
An apparent paradox
Mutation
Selection
Darwinian evolution uses selection on random
mutations to create complexity.
164
Thus stabilizing forward flight.
At the expense of extra weight and drag.
165
For minimum weight drag, (and other performance
issues) eliminate fuselage and tail.
166
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167
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168
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169
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170
Why do we love building robust systems from
highly uncertain and unstable components?
Robust control theory tells us why.
171
Sensors
Actuators
Control
Vehicle
Size ? Complexity
172

• Cheap
• Simple

• Unstable airframe
• Expensive sensors, actuators and computers
• More robust overall
• More efficient overall
• New fragilities

173
Stochastic vs. Deterministic

• Petzolds DAE (Differential Algebraic Equations)
  solver
• Total number of sigma-32 molecules per cell is
  very small (30 per cell)
• Number of free sigma-32 molecules per cell is
  even smaller (0.05 molecules per cell)
• Does it make sense to treat these quantities as
  concentrations?
• Stochastic models need to be considered
• Gillespies SSA algorithm

174
Regulation of Heat-Shock Response
175
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176
FREE
177
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178
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179
Research issues

• Current stochastic plots reproduce case where
  of folded proteins are held fixed
• Cannot incorporate protein folding dynamics
  because the SSA will be too slow to give any
  significant results
• Need to find a way to include deterministic
  dynamics into SSA
• Research plan multiscale stochastic simulation
  (combine Gillespie and Petzold)

180
37o
Log of E. Coli Growth Rate
42o
46o
21o
-1/T
181
Robustness/performance tradeoff?
37o
Log of E. Coli Growth Rate
42o
46o
21o
-1/T
182
Temperature
E. Coli
Membrane gels Transport slows
Proteins denature Thermal lysis
0
20
40
60
80
100
-1/T
(Scale is absolute, units are Celsius)
183
Mesophile
0
20
40
60
80
100
-1/T
184
Robustness/performance tradeoff?
Log Growth Rate
Nutrients, O2, pH, osmolarity, pressure,
185
E. Coli
Nonhalotolerant
0
30
Sodium ion concentration
186
Pressure
E. Coli
Barotolerant
200
600
1000
Pressure (atm)
187
Oxygen?
O2
188
Oxygen
aerobic
E. Coli
anaerobic
free radical stress
Facultative aerobe
5
10
1
.1
Air (21)
O2
189
Air (21)
5
10
1
.1
O2
190
Oxygen in mammals

• Obligate aerobes
• Oxygen varies widely between and within tissues
  but relatively constant over time
• Oxygen tension tightly regulated at multiple
  levels
• Oxygen tension more than a nutrient
• Important signal!!
• Development and regeneration
• Oxygen and free radical mediated transcriptional
  changes

191
Engineering interpretations

• Prediction oxygen tension is a critical
  component of in vitro modeling (cell and tissue
  culture)
• Clue components are often very sensitive to
  variables for which there is elaborate,
  expensive, and global regulation. Examples
  include
• Temperature and pH for mammalian cells
• Power supply in high performance electronics
• Fuel quality in high performance engines
• Frequency in frequency multiplexed radio or
  optical transmission or switching
• Etc.
• Free radical damage may be underrated in
  traditional tissue culture oxygen environment.

192
Dopamine producing neurons
10T1/2 Cell line
Low O2
Stains for
Tyrosine hydroxylase
Neurons
20 O2
193

• Major findings Fundamental changes in stem cell
  biology are induced by the level of oxygen
  surrounding stem cell cultures (M. Csete, MD, PhD
  UMich)
• Proliferation, programmed cell death, yield, type
  of daughter cells
• Verified in multiple systems CNS, neural crest,
  muscle satellites
• Implications for in vitro modeling in biology and
  medicine

194
lung
alveoli
artery
vein
brain
capillaries
mean tissue
venous blood
alveoli
arterial blood
Adult tissue
5
10
1
.1
O2
195
Oxygen?
tissue culture
mean tissue
aerobic
anaerobic
free radical stress
Adult tissue
Air (21)
5
10
1
.1
O2
196
Implications for cell cultures
normoxic
hyperoxic
hypoxic
tissue culture
mean tissue

• Free Radical Stress
• Signal

Air (21)
5
10
1
.1
O2
197
Dopamine producing neurons
10T1/2 Cell line
muscle
fat
bone
Low O2 (normoxic)
Stains for
Tyrosine hydroxylase
Neurons
20 O2

• Neurons from rat primary CNS stem cells
• Yellow (redgreen)dopaminergic

• Fat development from pluripotent stem cell lines

198
muscle fiber survival
Whole single fiber survival. Adult mouse muscle.
Ngt80.
199
Long-term cultured muscle fiber survival
100
10
1
Air (21)
5
10
1
O2