Cofactors

1
Polymerization and complex assembly
Autocatalytic feedback
Taxis and transport
Proteins
Complexity and
Core metabolism
Sugars
Catabolism
Amino Acids
Nucleotides
Precursors
Nutrients
Trans
Fatty acids
Genes
Co-factors
Carriers
Architecture
DNA replication
John Doyle John G Braun Professor Control and
Dynamical Systems, BioEng, and ElecEng Caltech
www.cds.caltech.edu/doyle
2
My interests
Multiscale Physics
Core theory challenges
Network Centric, Pervasive, Embedded, Ubiquitous
Systems Biology
3
Collaborators and contributors(partial list)

• Biology Csete,Yi, El-Samad, Khammash, Tanaka,
  Arkin, Savageau, Simon, AfCS, Kurata, Smolke,
  Gross, Kitano, Hucka, Sauro, Finney, Bolouri,
  Gillespie, Petzold, F Doyle, Stelling, Caporale,
• Theory Parrilo, Carlson, Murray, Vinnicombe,
  Paganini, Mitra Papachristodoulou, Prajna,
  Goncalves, Fazel, Liu, Lall, DAndrea, Jadbabaie,
  Dahleh, Martins, Recht, many more current and
  former students,
• Web/Internet Li, Alderson, Chen, Low, Willinger,
  Kelly, Zhu,Yu, Wang, Chandy,
• Turbulence Bamieh, Bobba, McKeown, Gharib,
  Marsden,
• Physics Sandberg, Mabuchi, Doherty, Barahona,
  Reynolds,
• Disturbance ecology Moritz, Carlson,
• Finance Martinez, Primbs, Yamada, Giannelli,

Current Caltech
Former Caltech
Other
Longterm Visitor
4
Thanks to

• NSF
• ARO/ICB
• AFOSR
• NIH/NIGMS
• Boeing
• DARPA
• Lee Center for Advanced Networking (Caltech)
• Hiroaki Kitano (ERATO)
• Braun family

5
Background progress

• Spectacular progress, both depth and breadth
• Biological networks
• Technological networks
• Mathematical foundations
• Remarkably consistent, convergent, coherent
• Role of protocols, architecture, feedback, and
  dynamics
• Yet seemingly persistent errors and confusion
  both within science between science and public
  policy

6
Unifying concepts

• Robustness
• Constraints

Ruthless oversimplification Terribly boring
7
Robust
Human complexity
Yet Fragile

• Efficient, flexible metabolism
• Complex development and
• Immune systems
• Regeneration renewal
• Complex societies
• Advanced technologies

• Obesity and diabetes
• Rich microbe ecosystem
• Inflammation, Auto-Im.
• Cancer
• Epidemics, war,
• Catastrophic failures

• Evolved mechanisms for robustness allow for, even
  facilitate, novel, severe fragilities elsewhere
• often involving hijacking/exploiting the same
  mechanism
• There are hard constraints (i.e. theorems with
  proofs)

8
Constraints as unifying concept

• Robust yet fragile is a hard constraint
• Complexity of systems due to constraints on
  robustness/evolvability rather than minimal
  functionality
• Architecture Constraints that deconstrain

9
Architecture is a central challenge

• The bacterial cell and the Internet have
• architectures
• that are robust and evolvable (yet fragile?)
• What does architecture mean here?
• What does it mean for an architecture to be
  robust and evolvable?
• Robust yet fragile?

10
fan-out of diverse outputs
universal carriers
fan-in of diverse inputs
Universal architectures
Diverse function

• Bowties for flows
• Hourglasses for control
• Robust and evolvable
• Architecture protocols constraints

Universal Control
Diverse components
11
The Internet hourglass
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
Ethernet
802.11
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
12
The Internet hourglass
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
TCP
IP
Ethernet
802.11
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
13
The Internet hourglass
Applications
IP under everything
Web
FTP
Mail
News
Video
Audio
ping
napster
TCP
IP
Ethernet
802.11
Satellite
Optical
Power lines
Bluetooth
ATM
Link technologies
14
Applications
Top of waist provides robustness to variety
and uncertainty above
TCP/ AQM
Bottom of waist provides robustness to
variety and uncertainty below
IP
15
Main bowtie in Internet S


Variety of files
Variety of files
packets

• All sender files transported as packets
• All files are reconstructed from packets by
  receiver
• All advanced technologies have protocols
  specifying knot of carriers, building blocks,
  interfaces, etc
• This architecture facilitates control, enabling
  robustness and evolvability
• It also creates fragilities to hijacking and
  cascading failure

16
Many bowties in Internet


Variety of files
Variety of files
packets


Applications




TCP


IP


17
Examples of
universal carriers

• Packets in the Internet
• 60 Hz AC in the power grid
• Lego snap
• Money in markets and economics
• Lots of biology examples (coming up)

18
Nested bowties advanced technologies
Everything is made this way cars, planes,
buildings, laptops,
19
Electric power
Variety of producers
Variety of consumers
20
Standard
interface
Variety of consumers
Variety of producers
Energy carriers

• 110 V, 60 Hz AC
• (230V, 50 Hz AC)
• Gasoline
• ATP, glucose, etc
• Proton motive force

21

• Carriers
• Precursors
• Trans
• 2CST

Bacterial bowties
Sugars

Fatty acids
Precursors
Co-factors
Catabolism
Amino Acids
Nucleotides
Genes
Proteins
Carriers
Trans
DNA replication
22

• Carriers
• Precursors
• Trans
• 2CST

Modules are less important than protocols the
rules by which modules interact.
Precursors
Carriers
Trans
23
Constraints
Precursors
Carriers
Trans
24


Variety of Ligands Receptors
Variety of responses
Transmitter
Receiver
Constraints That Deconstrain
(Gerhart and Kirschner)
Sugars

Fatty acids
Precursors
Catabolism
Co-factors
Amino Acids
Nucleotides
Genes
Proteins
Carriers
Trans
DNA replication
25
No variety
Huge Variety
Huge variety

• Virtually unlimited variability and
  heterogeneity (within and between organisms)
• E.g Time constants, rates, molecular counts and
  sizes, fluxes, variety of molecules,
• Very limited but critical points of homogeneity
  (within and between organisms)

26
No variety
Why?
Huge Variety
Huge variety

• Provides plug and play modularity between huge
  variety of input and output components
• Facilitates robust regulation and evolvability
  on every timescale (constraints that
  deconstrain)
• But has extreme fragilities to parasitism and
  predation (knots are easily hijacked or consumed)

27
Bacterial hourglass
Regulation of protein action
Regulation of protein levels
Core metabolism
Sugars

Fatty acids
Co-factors
Precursors
Catabolism
Amino Acids
Genes
Nucleotides
Proteins
Carriers
Trans
DNA replication
28
Regulatory hourglass
Huge variety of environments, metabolisms,
functions
Regulation of protein action
Regulation of protein levels
Huge variety of components
29
Huge variety
Environments, metabolisms, functions
Standardized mechanisms Highly conserved
Regulation of protein action
Regulation of protein levels
Huge variety
Components (genes)
Variety is within a specie (across time and
space) and of course between species.
30
Systems requirements functional,
efficient, robust, evolvable
Hard constraints Thermo (Carnot) Info
(Shannon) Control (Bode) Compute (Turing)
Architecture Constraints That Deconstrain
Constraints
Components and materials Energy, moieties
31
Environment Robust power generation
Architecture Carnot cycle (Combined cycle)
System Hard limits Entropy
Components Energy conserved
32
Environment Robust power generation
Architecture Carnot cycle (Combined cycle)
System Hard limits Entropy
Chance/choice Or Necessity?
Components Energy conserved
33
Electricity generation and consumption
From chance to necessity?
http//phe.rockefeller.edu/Daedalus/Elektron/
34
Environment Robust power generation
Chance/choice Or Necessity?
Similar architectures
System Hard limits Entropy

• Similar Efficiencies
• Overall (30-60)
• Mechanical ? Electrical (100)

Components Energy conserved
35
De Duve, Wachtershauser

• PMF
• DNA
• Proteins
• Lipids
• RNA
• ATP
• NTP and (pyro-)phospho-transfer
• Choice of ions? (Ca2, Na, K, Mg2)
• Choice of metals? (Fe, etc.)
• Thioesters
• Group transfer
• Electron transfer
• Catalysis
• Carbon, Nitrogen, Hydrogen,
• Energy, matter, small moieties

Chance/ Choice? Necessity
Necessity Physico-chemical
36
Necessity (Environment) Robustness of system
Chance/ choice Or Necessity?
Necessity (Theory) Hard limits on Robustness
Choice? Robust Architecture
Complexity?
Robustness
Necessity Physico-chemical
37
Hard constraints Thermo (Carnot) Info
(Shannon) Control (Bode) Compute (Turing)

• Assume architectures a priori
• Fragmented and incompatible
• Cannot be used as a basis for comparing
  architectures
• New unifications are encouraging

Constraints
38
Nuno C Martins and Munther A Dahleh, Feedback
Control in the Presence of Noisy Channels
Bode-Like Fundamental Limitations of
Performance. Nuno C. Martins, Munther A. Dahleh
and John C. Doyle Fundamental Limitations of
Disturbance Attenuation in the Presence of Side
Information (Both in IEEE Transactions on
Automatic Control)
http//www.glue.umd.edu/nmartins/
39
Fragile
Disturbance
-
ed-u
d
Remote Sensor
Control Channel
Plant
Sensor Channel
Control
Encode
remote control
benefits
stabilize
remote sensing
feedback
costs

• Good designs transform/manipulate robustness
• Subject to hard limits
• Unifies theorems of Shannon and Bode (1940s)
• Claim This is the most crucial (known) limit
  against which network complexity must cope

40
Bodes integral formula
Yet fragile
?
Robust
benefits
costs
41
d
ed-u
Disturbance
-
u
Plant
Cost of stabilization
Control
Cost of control
?
benefits
costs
42
-
ed-u
Cost of remote control
Plant
Control Channel
Control
benefits
costs
43
Disturbance
-
ed-u
d
Plant
Control Channel
Control
remote control
benefits
stabilize
feedback
costs
44
Disturbance
-
ed-u
d
Remote Sensor
Plant
Control Channel
Sensor Channel
Control
Encode
remote control
benefits
stabilize
remote sensing
feedback
costs
45
Disturbance
-
ed-u
d
Remote Sensor
Plant
Control Channel
Sensor Channel
Control
Encode
Benefit of remote sensing
benefits
costs
46
Disturbance
-
ed-u
d
Remote Sensor
Plant
Control Channel
Sensor Channel
Control
Encode
remote control
benefits
stabilize
remote sensing
feedback
costs
47
Disturbance
-
ed-u
d
Remote Sensor
Plant
Control Channel
Sensor Channel
Control
Encode
Bode/Shannon is likely a better p-to-p comms
theory to serve as a foundation for networks than
either Bode or Shannon alone.
48
Electric power network
Variety of producers
Variety of consumers

• Good designs transform/manipulate energy
• Subject (and close) to hard limits

49
Fragile
Disturbance
Control
-
ed-u
d
Remote Sensor
Control Channel
Plant
Sensor Channel
Control Channel
Control
Encode

• Robust designs transform/manipulate robustness
• Subject (and close) to hard limits
• Fragile designs are far away from hard limits and
  waste robustness.

50
Hard constraints Thermo (Carnot) Info
(Shannon) Control (Bode) Compute (Turing)

• Assume architectures a priori
• Fragmented and incompatible
• Cannot be used as a basis for comparing
  architectures
• New unifications are encouraging
• Robust/fragile is unifying concept

Constraints
51
Emergent complexity

• The most fragile details of complex systems will
  likely always be experimentally and
  computationally intractable.
• Fortunately, we care more about understanding,
  avoiding, and managing fragility (than about all
  its details)

52
Evolving evolvability?
Random Variation
Structured Selection
?
53
Random variation is harmful, yet
Variation
Structured Selection
Random, Small, Harmful
54
Polymerization and complex assembly
Autocatalytic feedback
Taxis and transport
Proteins
Core metabolism
Sugars
Catabolism
Amino Acids
Nucleotides
Precursors
Nutrients
Trans
Fatty acids
Genes
Co-factors
Carriers
DNA replication
Architecture
E. coli genome
55
Structured variation can be good
Structured Selection
Variation
Structured, Large, Beneficial
Architecture
56
Structured variation can be good
Not random
Structured Selection
Variation
Structured, Large, Beneficial
Architecture
57
Structured variation can be good

• Robust architectures facilitate change
• Small genotype ? ? large, functional phenotype ?
  
• (Wolves ? Dogs) ? regulatory regions
• Large (but functional) genotype ? are
  facilitated
• (Antibiotic resistance) Horizontal gene transfer

Structured Selection
Variation
Structured, Large, Beneficial
Architecture
58
This happened very fast (years)

• Robust architectures facilitate change
• Small genotype ? ? large, functional phenotype ?
  
• (Wolves ? Dogs) ? regulatory regions
• Large (but functional) genotype ? are
  facilitated
• (Antibiotic resistance) Horizontal gene transfer

59
Evolving evolvability?
Structured Selection
Structured Variation
facilitated structured organized
Architecture
60
Lego hourglass
Diverse function
Universal Control
control
Diverse components
assembly
61
Lego hourglass
Huge variety
Standardized mechanisms Highly conserved
control
assembly
Huge variety
62
Lego
Huge variety
Limited environmental uncertainty needs minimal
control
Standard assembly
Huge variety
63
Question what is the difference between
hourglass and bowtie here?
Diverse function
Standard assembly
Diverse components
This seems like a minimal place to address this
issue.
Variety of systems
Variety of bricks
Snap
64
The snap is a static interface specification.
Variety of systems
Variety of bricks
Snap
65
Diverse function
Assembly is a control process that evolves in
time, and respects the snap, but adds to it. It
inputs instructions and components and outputs
assembled systems.
Standard assembly
Diverse components
Instructions
Instructions
Instructions
66
With the snap and assembly, complex toys can be
created, and require additional layers of control.
Variety of systems
Variety of bricks
Snap
67
Random
Not random
Variety of systems
Variety of bricks
Snap
68
Lego hourglass
Uncertain environments
Require additional layers
control
assembly
Huge variety
69
NXT controller
Variety of actuators
Variety of sensors
Real- time control
70
fan-out of diverse outputs
universal carriers
fan-in of diverse inputs
Diverse function
Bowties andHourglasses
Universal Control
Diverse components
71
Variety of actuators
Variety of sensors
Control
Lego Bowties
Variety of systems
Variety of bricks
Snap
72
Random
Qualitatively unchanged by random rewiring
SOC Lego?
73
Not random
Almost certainly destroyed by random rewiring,
yet
Large structured rewirings are functional.
The essence of architecture.
74
Not random
Most of the complexity is in digital hardware and
software.
75
Variety
control
Lego hourglass
assembly
Variety
76
Visualization
Constraints Robustness of system
Hard limits Thermo (Carnot) Info
(Shannon) Control (Bode) Comp (Turing)
Feasible model
Necessity Components
77
Under-modeled reality
Feasible model
Feasible reality
Over-modeled reality
78
reality
Good architecture
model
79
Bad architecture
reality
model
Overconstrained
Underconstrained
80
Bad architecture
reality
model
Too complicated
81
Bad architecture
reality
Too simple (but not bad)
model
82
Good Theory
Proof of architecture
reality
Good architecture
Better model
83
Good Theory
Bad architecture
reality
Better model
Find counterexamples
84
codimension ? 8
The constraints all greatly reduce dimension
Hard to visualize in 2d as the ambient
dimension is enormous, and the codimension is
nearly infinite.
85
The constraints that deconstrain?

• More precisely constraints that minimally
  constrain
• But it implies more by adopting shared
  constraints bio and techno networks actually
  create alternatives that would not otherwise
  exist
• Thus it is sharing of constraints between systems
  that is a source of deconstraint and not
  captured very well in these cartoons

86
De Duve, Wachtershauser

• PMF
• DNA
• Proteins
• Lipids
• RNA
• ATP
• NTP and (pyro-)phospho-transfer
• Choice of ions? (Ca2, Na, K, Mg2)
• Choice of metals? (Fe, etc.)
• Thioesters
• Group transfer
• Electron transfer
• Catalysis
• Carbon, Nitrogen, Hydrogen,
• Energy, matter, small moieties

Chance/ Choice? Necessity
Necessity Physico-chemical