Co-factors
description
Transcript of Co-factors
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Genes
DNA replication
Trans*
John DoyleJohn G Braun Professor
Control and Dynamical Systems,BioEng, and ElecEng
Caltech
GGGDP GTP
GDPGTP
In
In
Out
P
Ligand
Receptor
RGS
www.cds.caltech.edu/~doyle
Collaborators and contributors(partial list)
Biology: Csete,Yi, Tanaka, Arkin, Savageau, Simon, AfCS, Kurata, Khammash, El-Samad, Gross, Kitano, Hucka, Sauro, Finney, Bolouri, Gillespie, Petzold, F Doyle, Stelling, …
Theory: Parrilo, Carlson, Paganini, Papachristodoulou, Prajna, Goncalves, Fazel, Liu, Lall, D’Andrea, Jadbabaie, Dahleh, Martins, Recht, many more current and former students, …
Web/Internet: Li, Alderson, Chen, Low, Willinger, Vinnicombe, Kelly, Zhu, Yu, Wang, Chandy, …
Turbulence: Bamieh, Bobba, Gharib, Marsden, …Physics: Mabuchi, Doherty, Barahona, Reynolds,Disturbance ecology: Moritz, Carlson,…Finance: Martinez, Primbs, Yamada, Giannelli,…Engineering CAD: Ortiz, Murray, Schroder, Burdick, …
Current Caltech Former Caltech OtherLongterm Visitor
MultiscalePhysics
SystemsBiology
Network Centric,Pervasive,Embedded,Ubiquitous
Core theory
challenges
SystemsBiology
Network Centric,Pervasive,Embedded,Ubiquitous
Core theory
challenges
Today’s focus
Core theory challenges
Core theory challenges
Core theory challenges
SystemsBiology
Pervasive,Networked,Embedded
…is on stories you are unlikely
to hear from anyone else.
Today’s focus
Caution: maybe others are more sensible than I
am.
Core theory challenges
Core theory challenges
Core theory challenges
Consistent, coherent, convergent understanding.
Status
Dramatic recent
progress in laying the
foundation.
Yet also striking increase in
unnecessary confusion???
SystemsBiology
Pervasive,Networked,Embedded
Remarkably common core
theoretical challenges.
A look back and forward
• The Internet architecture was designed without a “theory.”
• Many academic theorists told the engineers it would never work.
• We now have a nascent theory that confirms that the engineers were right.
• Parallel stories exist in “theoretical biology.”• For future networks and other new technologies,
as well as systems biology of the cell, organism and brain, let’s hope we can avoid a repeat of this history.
Robust yet fragile (RYF)
• The same mechanisms responsible for robustness to most perturbations
• Allows possible extreme fragilities to others
Robust
1. Efficient, flexible
metabolism
2. Complex development
and
3. Immune systems
4. Regeneration & renewal
5. Complex societies
1. Obesity and diabetes
and
2. Rich parasite
ecosystem
3. Auto-immune disease
4. Cancer
5. Epidemics, war,
genocide, …
Yet Fragile
Human robustness and fragility
Robust1. Efficient, flexible metabolism2. Complex development and3. Immune systems4. Regeneration & renewal 5. Complex societies
6. Advanced technologies
1. Obesity and diabetes and 2. Rich parasite ecosystem 3. Auto-immune disease4. Cancer5. Epidemics, war, …
6. ?????
Yet Fragile
What about technology?
Robust1. Efficient, flexible metabolism2. Complex development and3. Immune systems4. Regeneration & renewal 5. Complex societies
6. Advanced technologies
1. Obesity and diabetes and 2. Rich parasite ecosystem 3. Auto-immune disease4. Cancer5. Epidemics, war, …
6. ?????
Yet Fragile
What about technology?
TCP/IP robustness/evolvability on many timescales
• Shortest: File transfers from different users– Application-specific navigation (application)
– Congestion control to share the net (TCP)
– Ack-based retransmission (TCP)
• Short: Fail-off of routers and servers– Rerouting around failures (IP)
– Hot-swaps and rebooting of hardware
• Long: Rearrangements of existing components– Applications/clients/servers can come and go
– Hardware of all types can come and go
• Longer: New applications and hardware– Plug and play if they obey the protocols
TCP/IP fragility on many timescales
• Shortest: File transfers from different users– End systems w/o congestion control won’t
fairly share
• Short: Fail-on of components– Distributed denial of service– Black-holing and other fail-on cascading
failures
• Long: Spam, viruses, worms
• Longer: No economic model for ISPs?
Robust yet fragile (RYF)
• The same mechanisms responsible for robustness to most perturbations
• Allows possible extreme fragilities to others
• Usually involving hijacking the robustness mechanism in some way
• Ubiquitous in biology and advanced technology
• High variability (and thus power laws)
Nightmare? Biology: We might accumulate more complete
parts lists but never “understand” how it all works.
Technology: We might build increasingly complex and incomprehensible systems which will eventually fail completely yet cryptically.
Nothing in the orthodox views of complexity says this won’t happen (apparently).
HOPE?
Interesting systems are robust yet fragile.
Identify the fragility, evaluate and protect it.
The rest (robust) is “easy”.
Nothing in the orthodox views of complexity says this can’t happen (apparently).
Working systems but no “proofs’
Spectacular progress….
Progress• Understanding mice and elephants• “Primal-dual” decompositions for layering• Global stability analysis with delays• New solutions to “classic” problems
Many challenges• Rigorous multiscale models• Latency-based utilities• Wireless, MANETs• Interdomain routing
Hosts
Routers
packets
“FAST” theoryArbitrarily complex network• Topology• Number of routers and hosts• Nonlinear• Delays
Short proof• Global stability• Equilibrium optimizes aggregate user utility
Papachristodoulou, Li
I2LSR, SC2004 Bandwidth Challenge
OC48
OC192
November 8, 2004 Caltech and CERN transferred 2,881 GBytes in one hour (6.86Gbps) between Geneva - US - Geneva (25,280 km) through LHCnet/DataTag, Abilene and CENIC backbones using 18 FAST TCP streams
Harvey Newman’s group, Caltechhttp://dnae.home.cern.ch/dnae/lsr4-nov04
Yet growing confusion…
…sorry, but in the interest of scientific hygiene, we have to admit
that all is not well…
One ofthe most-read papers ever on
the Internet!
100
101
102
103
100
101
102
power-law degrees
High degree hub-like core
“Scale-free” networks and the “Achilles’ heel” of the Internet
High degree hub-like core
Delete these “hubs” and the network disconnects!
Delete “non-hubs” with little effect!
Robust yet fragile!?!?!?
SOX
SFGP/AMPATH
U. Florida
U. So. Florida
Miss StateGigaPoP
WiscREN
SURFNet
Rutgers U.
MANLAN
NorthernCrossroads
Mid-AtlanticCrossroads
Drexel U.
U. Delaware
PSC
NCNI/MCNC
MAGPI
UMD NGIX
DARPABossNet
GEANT
Seattle
Sunnyvale
Los Angeles
Houston
Denver
KansasCity
Indian-apolis
Atlanta
Wash D.C.
Chicago
New York
OARNET
Northern LightsIndiana GigaPoP
MeritU. Louisville
NYSERNet
U. Memphis
Great Plains
OneNetArizona St.
U. Arizona
Qwest Labs
UNM
OregonGigaPoP
Front RangeGigaPoP
Texas Tech
Tulane U.
North TexasGigaPoP
TexasGigaPo
P
LaNet
UT Austin
CENIC
UniNet
WIDE
AMES NGIX
PacificNorthwestGigaPoP
U. Hawaii
PacificWave
ESnet
TransPAC/APAN
Iowa St.
Florida A&MUT-SWMed Ctr.
NCSA
MREN
SINet
WPI
StarLight
IntermountainGigaPoP
Abilene BackbonePhysical Connectivity
0.1-0.5 Gbps0.5-1.0 Gbps1.0-5.0 Gbps5.0-10.0 Gbps
Internet router-level topology
Internet router-level topology
0.1-0.5 Gbps0.5-1.0 Gbps1.0-5.0 Gbps5.0-10.0 Gbps
0.1-0.5 Gbps0.5-1.0 Gbps1.0-5.0 Gbps5.0-10.0 Gbps
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101
102
103
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101
102
power-law degrees
Low degree mesh-like core
Completely different networks can have the same node degrees.
High variability edge systems
rank
100
101
102
103
100
101
102
identical power-law degrees
High degree hub-like core
Low degree mesh-like core
Completely different networks can have the same node degrees.
• Low degree core• High performance
and robustness
• High degree “hubs”• Poor performance
and robustness
See PNAS, Sigcomm, TransNet papers for details.
Nothing like the real Internet.
Core theory challenges
• Hard constraints
• Simple models
• Short proofs
• Architecture
Architecture?
• “The bacterial cell and the Internet have – architectures– that are robust and evolvable”
• What does “architecture” mean?
• What does it mean for an “architecture” to be robust and evolvable?
• Robust yet fragile?
• Rigorous and coherent theory?
Hard constraints
On systems and their components
• Thermodynamics (Carnot)
• Communications (Shannon)
• Control (Bode)
• Computation (Turing/Gödel)
• Fragmented and incompatible
• We need a more integrated view and have the beginnings
Assume different architectures a priori.
Hard constraints (progress)
• Thermodynamics (Carnot)
• Communications (Shannon)
• Control (Bode)
• Computation (Turing/Gödel)
• We need a more integrated view and have
the beginnings
Work in progress
• Thermodynamics (Carnot)
• Communications (Shannon)
• Control (Bode)
• Computation (Turing/Gödel)
• We need a more integrated view and have
the beginnings
• Robust yet fragile systems
• Proof complexity implies problem fragility (!?!)
• Designing for robustness and verifiability are compatible objectives
• Hope for a unifying framework?
• Integrating simple models and short proofs?
Short proofs (progress)
Simple models (challenges)
• Rigorous connections between– Multiple scales – Experiment, data, and models
• Essential starting point for short proofs• Microscopic state: discrete, finite, enormous
– Packet level – Software, digital hardware– Molecular-level interactions (biology)
• Macroscopic behavior: robust yet fragile
Architecture?
• “The bacterial cell and the Internet have – architectures– that are robust and evolvable”
• What does “architecture” mean?
• What does it mean for an “architecture” to be robust and evolvable?
• Robust yet fragile?
• Rigorous and coherent theory?
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Polymerization and complex
assemblyProteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
The architecture of the cell.
Feathers and
flapping?
Or lift, drag, propulsion, and control?
The danger of biomemetics
Wilbur Wright on CWilbur Wright on Control,ontrol, 1901 1901• “We know how to construct airplanes.” (lift and drag)• “Men also know how to build engines.” (propulsion)• “Inability to balance and steer still confronts students of the flying problem.” (control)• “When this one feature has been worked out, the age of flying will have arrived, for all other difficulties are of minor importance.”
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
DnaK
DnaK
ftsH
Lon
Regulation of
protein levels
Regulation of protein action
PMF
++
++
From Taylor, Zhulin, Johnson
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Carriers
Core metabolism
Genes
Regulation & control
Trans*
Lessons from the cell and Internet
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Polymerization and complex
assemblyProteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
The architecture of the cell.
Bowtie: Flow of energy and materials
Hourglass
Organization of control
Universal organizational structures/architectures
The Internet hourglass
Web FTP Mail News Video Audio ping napster
Applications
Ethernet 802.11 SatelliteOpticalPower lines BluetoothATM
Link technologies
The Internet hourglass
IP
Web FTP Mail News Video Audio ping napster
Applications
TCP
Ethernet 802.11 SatelliteOpticalPower lines BluetoothATM
Link technologies
The Internet hourglass
IP
Web FTP Mail News Video Audio ping napster
Applications
TCP
Ethernet 802.11 SatelliteOpticalPower lines BluetoothATM
Link technologies
IP under everything
IP oneverything
Bio: Huge variety of environments, metabolismsInternet: Huge variety of applications
Huge variety of components
Bio: Huge variety of environments, metabolismsInternet: Huge variety of applications
Huge variety of components
Both components and applications are
highly uncertain.
Bio: Huge variety of environments, metabolismsInternet: Huge variety of applications
Huge variety of components
Huge variety of genomesHuge variety of physical networks
Bio: Huge variety of environments, metabolismsInternet: Huge variety of applications
Huge variety of components
Huge variety of genomesHuge variety of physical networks
Feedback control
Identical control
architecture
Hourglass architectures
5:Application/function: variable supply/demand
4:TCP
3:IP
2:Potential physical network
1:Hardware components
4:Allosteric
3:Transcriptional
TCP/IP Metabolism/biochem
FeedbackControl
Bowtie: Flow of energy and materials
Hourglass
Organization of control
Universal organizational structures/architectures
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Polymerization and complex
assemblyProteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
The architecture of the cell.
EnvironmentEnvironment EnvironmentEnvironment
Huge Variety
Huge Variety
Bacterial cell
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Pre
curs
ors
Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Huge Variety
100same
in allorganisms
20same in all cells
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Pre
curs
ors
Carriers
Core metabolism
Nested bowties
Catabolism
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
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
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
ATP
Autocatalytic
NADH
Pre
curs
ors
Carriers
TCAPyr
Oxa
Cit
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP
Gly3p
13BPG
3PG
2PG
ATP
NADH
Pre
curs
ors
Carriers
20 same in all cells
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Regulatory
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
This is still a cartoon. (Cartoons are useful.)
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
If we drew the feedback loops the diagram would be unreadable.
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
ACA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG ACA
This can be modeled to some extent as a graph.
This cannot.
Unfortunate and unnecessary confusion results from not “getting” this.
( )
Mass &Reaction
Mass&Energy Energyflux
Balance
dxSv x
dt
d
dt
Stoichiometry plus regulation
Biology is not a graph.
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
( )
Mass &Reaction
Energyflux
Balance
dxSv x
dt
Stoichiometry matrix
S
Regulation of enzyme levels by transcription/translation/degradation
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
Oxa
Cit
ACA
( )
Mass &Reaction
Energyflux
Balance
dxSv x
dt
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
( )
Mass &Reaction
Energyflux
Balance
dxSv x
dt
Allosteric regulation of enzymes
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Mass &Reaction
( ) Energyflux
Balance
dxSv x
dt
Allosteric regulation of enzymes
Regulation of enzyme levels
Regulation of protein levels
Regulation of protein action
Allosteric regulation of enzymes
Regulation of enzyme levels
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Not to scale
essential: 230 nonessential: 2373 unknown: 1804 total: 4407
http://www.shigen.nig.ac.jp/ecoli/pec
Gene networks?
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Genes
DNA replication
Trans*
Regulation & control E. coli
genome
Trans*
Viruses exploit the universal bowtie/hourglass structure to hijack the cell machinery.
Fragility example: Viruses
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Proteins
Nut
rien
ts
Core metabolism
Genes
DNA replication
Trans*
Viruses exploit the universal bowtie/hourglass structure to hijack the cell machinery.
Fragility example: Viruses
Viralgenes
Viralproteins
Pre
curs
ors
Carriers
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
tsRegulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Regulation & control
Carriers
Core metabolism
Genes
DNA replicationRegulation & control
Trans*
Regulation & control
Multicellularity
Robustness:Efficient and flexible metabolism
Transport
Glucoseand
Oxygen
Autocatalytic and regulatory feedback
Cell
Cell
CellCell
CellCell
Cell
CellCell
Fragility: Obesity and diabetes
Robust
1. Efficient, flexible
metabolism
2. Complex development
and
3. Immune systems
4. Regeneration & renewal
5. Complex societies
1. Obesity and diabetes
and
2. Rich parasite
ecosystem
3. Auto-immune disease
4. Cancer
5. Epidemics, war,
genocide, …
Yet Fragile
Human robustness and fragility
Co-factors
Fatty acids
Sugars
Nucleotides
Amino Acids
Catabolism
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback Taxis and transport
Nut
rien
ts
Carriers
Core metabolism
Genes
DNA replication
Trans*
Regulation & control E. coli
genome
Allosteric regulation
Regulation of enzyme levels
Bio: Huge variety of environments, metabolisms
Huge variety of components
Huge variety of genomes
Universal control
architecture
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Allosteric
Trans*
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
DnaK
DnaK
ftsH
Lon
Regulation of
protein levels
Regulation of protein action
5:Application/function: variable supply/demand
4:TCP
3:IP
2:Potential physical network
1:Hardware components
4:Allosteric
3:Transcriptional
TCP/IP Metabolism/biochem
FeedbackControl
Protocols and modules
• Protocols are the rules by which modules are interconnected
• Protocols are more fundamental to “modularity” than modules
IP IPIP IP IP
TCP
Application
TCP
Application
TCP
Application
RoutingProvisioning
Ver
tica
l dec
ompo
siti
onP
roto
col S
tack
Each layer can evolve independently provided:
1. Follow the rules2. Everyone else does
“good enough” with their layer
IP IPIP IP IP
TCP
Application
TCP
Application
TCP
Application
RoutingProvisioning
Horizontal decompositionEach level is decentralized and asynchronous
Bowtie: Flow of energy and materials
Hourglass
Organization of control
Universal organizational structures/architectures
Robust yet fragile (RYF)
• The same mechanisms responsible for robustness to most perturbations
• Allows possible extreme fragilities to others
• Usually involving hijacking the robustness mechanism in some way
• High variability (and thus power laws)
Facilitate regulation on multiple time scales:1. Fast: allostery2. Slower: transcriptional regulation (by regulated
recruitment) and degradation3. Even slower: transfer of genes4. Even slower: copying and evolution of genes
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Carriers
Genes
DNA replication
Trans*
Nutrient
Allosteric regulation
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback
Regulation & control
Carriers
Genes
DNA replicationRegulation & control
Trans*
Nutrient
5:Apps: Supply and demand of packet flux
4:TCP: robustness to changing supply/demand, and packet losses
3:IP: routes on physical network, rerouting for router losses
2:Physical: Raw physical network
1:Hardware catalog: routers, links, servers, hosts,…
5:Apps:supply and demand of nutrients and products
4:Allosteric regulation of enzymes: robust to changing supply/demand
3:Transcriptional regulation of enzyme levels
2:Genome: Raw potential stoichiometry network
1:Hardware catalog: DNA, genes, enzymes, carriers,…
TCP/IP Metabolism/biochem
Robust yet fragile (RYF)
• The same mechanisms responsible for robustness to most perturbations
• Allows possible extreme fragilities to others
• Usually involving hijacking the robustness mechanism in some way
• High variability (and thus power laws)
Facilitate regulation on multiple time scales:1. Fast: allostery2. Slower: transcriptional regulation (by regulated
recruitment) and degradation3. Even slower: transfer of genes4. Even slower: copying and evolution of genes
TCP/IP robustness/evolvability on many timescales
• Shortest: File transfers from different users– Application-specific navigation (application)
– Congestion control to share the net (TCP)
– Ack-based retransmission (TCP)
• Short: Fail-off of routers and servers– Rerouting around failures (IP)
– Hot-swaps and rebooting of hardware
• Long: Rearrangements of existing components– Applications/clients/servers can come and go
– Hardware of all types can come and go
• Longer: New applications and hardware– Plug and play if they obey the protocols
TCP/IP fragility on many timescales
• Shortest: File transfers from different users– End systems w/o congestion control won’t
fairly share
• Short: Fail-on of components– Distributed denial of service– Black-holing and other fail-on cascading
failures
• Long: Spam, viruses, worms
• Longer: No economic model for ISPs?
Robustness/evolvability on many timescales• Shortest to short: Fluctuations in supply/demand
– Allosteric regulation of enzymes (4)
– Expression of enzyme level (3)
• Short: Failure/loss of individual enzyme molecules (3)– Chaperones attempt repair, proteases degrade
– Transcription makes more
• Long: Incorporating new components– Acquire new enzymes by lateral gene transfer
• Longer: Creating new applications and hardware– Duplication and divergent evolution of new enzymes
that still obey basic protocols
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Carriers
Genes
DNA replication
Trans*
Nutrient
Allosteric regulation
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Polymerization and complex
assembly
Proteins
Pre
curs
ors
Autocatalytic feedback
Regulation & control
Carriers
Genes
DNA replicationRegulation & control
Trans*
Nutrient
Robustness/evolvability on many timescales• Shortest to short: Fluctuations in supply/demand
– Allosteric regulation of enzymes (4)– Expression of enzyme level (3)
• Short: Failure/loss of individual enzyme molecules (3)– Chaperones attempt repair, proteases degrade – Transcription makes more
• Long: Incorporating new components– Acquire new enzymes by lateral gene transfer
• Longer: Creating new applications and hardware– Duplication and divergent evolution of new enzymes
that still obey basic protocols
Polymerization and complex
assembly
Proteins
Autocatalytic feedback
Regulation & control
Genes
DNA replication
Trans*
DnaK
rpoH
Lon
Other operons
motif
DnaK
rpoH
Lon
DnaK
Lon
Heat
degradation
folded unfolded
Other operons
motif
RNAP
DnaKRNAP
DnaK
rpoH
FtsH Lon
Heat mRNA
Other operons
Lon
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
Other operons
DnaK
DnaK
ftsH
Lon
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
Other operons
DnaK
DnaK
ftsH
Lon
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
DnaK
DnaK
ftsH
Lon
Regulation of
protein levels
Regulation of protein action
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
Other operons
DnaK
DnaK
ftsH
Lon
DnaK
rpoH
Lon
Other operons
motif
Robustness/evolvability on many timescales• Shortest to short: Fluctuations in supply/demand
– Allosteric regulation of enzymes (4)– Expression of enzyme level (3)
• Short: Failure/loss of individual enzyme molecules (3)– Chaperones attempt repair, proteases degrade – Transcription makes more
• Long: Incorporating new components– Acquire new enzymes by lateral gene transfer
• Longer: Creating new applications and hardware– Duplication and divergent evolution of new enzymes
that still obey basic protocols
What if a pathway is needed?
But isn’t there?
Slower time scales ( hours-days), genes are transferred between organisms.
This is only possible with shared protocols.
Robustness/evolvability on many timescales• Shortest to short: Fluctuations in supply/demand
– Allosteric regulation of enzymes (4)– Expression of enzyme level (3)
• Short: Failure/loss of individual enzyme molecules (3)– Chaperones attempt repair, proteases degrade – Transcription makes more
• Long: Incorporating new components– Acquire new enzymes by lateral gene transfer
• Longer: Creating new applications and hardware– Duplication and divergent evolution of new
enzymes that still obey basic protocols
Slowest time scales ( forever), genes are copied and mutate to create new enzymes.
Evolving evolvability?
VariationVariation Selection
?
Evolving evolvability?
VariationVariation Selection
“facilitated”“structured”“organized”
Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Robust?: Fragile to fluctuations Evolvable?: Hard to change
Biochem fragility on many timescales
• Shortest: Fluctuations in demand/supply that exceeds regulatory capability (e.g. glycolytic oscillations)
• Short: Fail-on of components– Indiscriminate kinases, loss of repression, …– Loss of tumor suppressors
• Long: Infectious hijacking
• Longer: Diabetes, obesity
Robust yet fragile (RYF)
• The same mechanisms responsible for robustness to most perturbations
• Allows possible extreme fragilities to others
• Are there robustness/fragility “conservation laws”?
Hard constraints
• Thermodynamics (Carnot)
• Communications (Shannon)
• Control (Bode)
• Computation (Turing/Gödel)
• Consider simplest case with distributed sensing and actuation and nontrivial communications and controls
-e=d-u
Control
ControlChannel
u
CC
Cost of remote control
Actuator
Minimal networking issues:• Real-time control of (possibly unstable) plant subject to disturbances• Actuation may be remote, necessitating control over a communication channel.• (For simplicity, won’t show actuator explicitly in sequel…)
Disturbance
d
Plant+local
sensor
Summary
Disturbance-e=d-u
Control SensorChannel Encode
Plant+local
sensorRemoteSensor
dd
r
ControlChannel SC
u
CC
More networking issues:• Remote real-time control of (possibly unstable) plant subject to disturbances• Network may provide an “early warning” via remote sensing of disturbance• Communication may be involved in both remote control and sensing
Benefit of remote sensing
Disturbance-e=d-u
Control SensorChannel Encode
Plant+local
sensorRemoteSensor
dd
r
ControlChannel
SC
u
CC
ES
D
Summary
Minimal networking “hard constraints”:• What are the benefits of remote sensing using a channel to signal to a controller?• What are the costs of remote control over a channel?• Express results in terms relevant to control of the plant, such as sensitivity:
or logS S
Disturbance-e=d-u
Control SensorChannel Encode
Plant RemoteSensor
dd
r
ControlChannel SC
u
CC
log S d
log S d
CClog( )a
0SC d u r
d u r
max
Benefit of control
Costs of:
Stabilization
Remote actuation
Feedback Benefit of remote sensing
Need high capacity
and low latency
/S E D Results
Attenuation of
disturbance
Cost of stabilization
log|S| “benefit” has an interpretation as entropy reduction from disturbance to error.
log S d
log S d
log( )a
-e=d-u
Control
Plant+local
sensor
u
• This benefit has various limits and costs, which in simple local control not involving remote sensing or actuation is just
Amplification of
disturbance
• Note that feedback control can thus give no net attenuation of disturbances, but simply moves log|S| around…
d /S E D
log S d
log S d
CClog( )a
0SC d u r
d u r
max
Benefit of control Costs of:
Stabilization
Remote actuation
Feedback Benefit of remote sensing
Need high capacity
and low latency
New results
The benefits of control are:• Aggravated by plant instability and remote actuation• Enhanced by remote sensing
Hard bounds explicitly depends on both capacity and latency of communication
Disturbance-e=d-u
Decode Channel
u
Encode
d delayd
delay
rCapacity C
As ,
( ) 0
d
h D C e
( ) ( )h E h D C
Features of the theory:1. Hard bounds2. Achievable (assumptions)3. Solution decomposable (assumptions)
ShannonRecall
r = total delay of encoding, decoding, and channeld =disturbance arrival delay from where it is remotely sensed
*
* The interpretation of depends on the details of the model.
Disturbance-e=d-u
Decode Channel
u
Encode
d delayd
delay
rCapacity C
As ,
( ) 0
d
h D C e
( ) ( )h E h D C
ShannonRecall
This is a nonstandard way of describing the results but will be convenient later.r = total delay of encoding, decoding, and channeld =disturbance arrival delay from where it is remotely sensed
Disturbance-e=d-u
Decode &Control
Channel
u
Encode
d delayd
delay
rCapacity C
( ) ( )h E h D C
Shannon
• We can think of this as a simple “network” control problem with a disturbance that can be remotely sensed.• A “controller” decodes a signal sent over a noisy channel and attempts to make the error small.• The entropy reduction in the error is bounded by the channel capacity.• We’ll return to this after motivating more complex control…
Biology motivation
• Illustrate hard constraints on feedback control
• Except in core metabolism, efficiency is not a dominant constraint
• In general, biological complexity is driven by robustness and control
• Start with illustration of results due to Bode circa 1940…
Metab
oliteE
nzym
e
kx
1 1
max
( ) ( )
( )1
( )
k k k k k k
m
x V x V x
VV x t
t
x
K
x
max( )1
( )m
VV x t
K
x t
( )x t
1 1( )k kV x
1kx
( )k kV xkx
Thermodynamics and metabolism
kx
1 1
max
( ) ( )
( )1
( )
k k k k k
m
x V x V x
VV x t
K
x t
1 1( )k kV x
( )k kV x
nx
1 0 1 1
max0
( ) ( )
( )
( )1
n
d
fb
x V x V x
VV x t
hx t t
K
perturbation
Product inhibition
Yi, Ingalls, Goncalves, Sauro
nx
0 ( )nV x
h = [0 1 2 3]
0 5 10 15 200.8
0.85
0.9
0.95
1
1.05
Time (minutes)
[AT
P]
h = 2
h = 1
h = 0
1 1 1 0
max0
( ) ( )
( )
( )1
n
d
fb
x V x V x
VV x t
x t t
K
h
Step increase in demand for product or “ATP.”
h = 3
Ideal product concentration
nxperturbation
nx
0 ( )nV x
0 5 10 15 20Time
h = 3
h = 2
h = 1
h = 0
Tighter steady-stateregulation
Transients, Oscillations
Higher feedback “gain”
It is well-known that many biological regulatory networks can oscillate, and presumably many more will be discovered.
0 5 10 15 20Time
h = 3
h = 2
h = 1
h = 0
Tighter steady-stateregulation
Transients, Oscillations
• Are these tradeoffs an artifice of this model?• Does it matter if the model is nonlinear, stochastic, distributed, PDEs, etc? Does it depend on the model at all?• Are these tradeoffs due to a frozen accident of evolution and not an absolute necessity?
• The answer to all these questions is no.
0 5 10 15 200.8
0.85
0.9
0.95
1
1.05
Time (minutes)
[AT
P]
h = 3
h = 0
Time response)
Fourier
Transform
of error
h hS x = F(
0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Lo
g(S
n/S
0)
h = 3
h = 0
Spectrum
0log loghS S
Ideal
0 5 10 15 200.8
0.85
0.9
0.95
1
1.05
Time (minutes)
[AT
P]
h = 3
h = 0
0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Lo
g(S
n/S
0)
h = 3
h = 0
Spectrum
Time response
Robust
Yet fragile
0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Lo
g(S
n/S
0)
h = 3
h = 0 Robust
Yet fragile
0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Lo
g(S
n/S
0)
h = 0 Robust
Yet fragile
log ) ?nx d constant F(
0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Lo
g(S
n/S
0)
h = 3
h = 2
h = 1
h = 0
log )nxF(
Tighter steady-stateregulation
Transients, Oscillations
log )nx d constant F(
Theorem
log )nx d constant F(
log|S |
Tighter regulation
Transients, Oscillations
Biological complexity is dominated by the evolution of
mechanisms to more finely tune this robustness/fragility tradeoff.
This tradeoff is a law.
log S d
log S d
benefits costs
log S d
log S d
• benefits = attenuation of disturbance• goal: make this as negative as possible
cost = amplificationgoal: make this small
Constraint:
-e=d-u
Control
uPlant
d
delay
u
log 0S d
Bode
ES
D
• What helps or hurts this tradeoff?• Helps: remote sensing• Hurts: instability, remote control
Control demo
0log ( ) /S d L
L
Note: assumes continuous time
• A classic illustration of instability and control, the simple inverted pendulum experiment, illustrates the essential point.
• Here the pendulum is the plant, and the human is the controller. The experiment can be done with sticks of different lengths or with an extendable pointer, holding the proximal tip between thumb and forefinger so that it is free to rotate but not otherwise slip.
• Unstable plants are intrinsically more difficult to control than stable ones, and are generally avoided unless the instability confers some great functional advantage, which it often does.
• With the controlling hand fixed, this system has two equilibria, down and up, which are stable and unstable, respectively. By watching the distal tip and controlling hand motion, the up case can be stabilized if the stick is long enough. • For an external disturbance, imagine that someone is throwing objects at the stick and you are to move so that the stick remains roughly vertical and avoids the thrown object. Alternatively, imagine that the distal tip is to track some externally driven motion.• You will soon find that it is much easier to control the distal tip down than up, even though the components in both cases are the same. • Because the up configuration is unstable, certain hand motions are not allowed because they produce large, unstable tip movements. This presents an obstacle in the space of dynamic hand movements that must be avoided, making control more difficult.
• If you make the stick shorter, it gets more unstable in the up case, evident in the short time it takes the uncontrolled stick to fall over. • Shorter pendulums get harder and ultimately impossible to control in the up case, while length has little such effect on the down case. • Also, the up stick cannot be stabilized for any length if only the proximal tip is watched, so the specific sensor location is crucial as well. • This exercise is a classical demonstration of the principle that the more unstable a system the harder it is to control robustly, and control theory has formally quantified this effect in several ways.
-e=d-u
Control
uPlant
d
delay
u
log S dL
/ L
ES
D
L
Freudenberg and Looze, 1984
a
-e=d-u
Control
uPlant
d
delay
u
log S d
Bode
a
ES
D
log S d
benefits costs stabilize
a
-e=d-u
Control
uPlant
d
delay
u
log S d
Bode
a
ES
D
log S d
benefits costs stabilize
Negative is good
-e=d-u
Control
uPlant
d
delay
u
Bode
a
ES
D
alog S d
log S d
1. Hard bounds2. Achievable (assumptions)3. Solution decomposable (assumptions)
With Martins and Dahleh
New theory, including comms..
0log ( ) /S d L
Switch to discrete time.
0
1log ( ) log( ) log 1 1/S d a L
L
Disturbance-e=d-u
Decode Channel
u
Encode
d delayd
delay
rCapacity C
As , ( ) 0d h D C e
( ) ( )h E h D C
1. Hard bounds2. Achievable (assumptions)3. Solution decomposable (assumptions)
ShannonRecall
-e=d-u
Control
uPlant
d
delay
u
Disturbance-e=d-u
Decode Channel
u
Encode
d delayd
delay
rCapacity C
log log( )S d a As , ( ) 0d h D C e
( ) ( )h E h D C
1. Hard bounds2. Achievable (assumptions)3. Solution decomposable (assumptions)
Incompatible assumptions (for 50+ years).
Bode Shannon
Disturbance-
Control Channel
u
Encode
RemoteSensor
d delayd
delay
u
delay
rCapacity C
-e=d-u
Control
uPlant
d
delay
u
0 d u r
C d u r
log log( )S d a
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
What is the benefit to control of remote sensing?
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
0log log( )
d u rS d a
C d u r
Cost of stabilization
Benefits of remote sensing
Need relatively low
latency
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
0log log( )
d u rS d a
C d u r
1. Hard bounds2. Achievable (assumptions)3. Solution decomposable (assumptions)
New unified comms, controls, and stat mech.?
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
0log log( )
d u rS d a
C d u r
Claim (or irresponsible speculation?):1. Biological complexity is dominated
by the tradeoffs which are captured (simplistically) in this theorem.
2. Ditto for techno-networks.
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
0log log( )
d u rS d a
C d u r
What is likely (though not agreed upon) is Bode/Shannon is a much better point-to-point communication theory to serve as a foundation for networks than either Bode or Shannon alone.
log( )alog S d
log( )a
log S d
benefits costs
Disturbance-e=d-u
Control Channel
u
Encode
PlantRemoteSensor
d delayd
delay
u
delay
rCapacity C
C
Benefit of remote sensing
-e=d-u
Control
Plant
ControlChannel
u
CC
Cost of remote control
What is the cost if the control action must be done remotely and communication to an actuator is over a channel with capacity CC ?
Actuator
-e=d-u
Control
Plant ControlChannel
u
CC
Cost of remote control
benefits costs
log( )alog S d
CC
(Note: needs directed information…)
Note: Actuator not shown.
-e=d-u
Control
Plant ControlChannel
u
CCCost of remote control
Benefit of remote sensing
log( )alog S d
log S d
benefits costs
C
log( )alog S d
CC
Putting it all together
Disturbance-e=d-u
ControlSensor
ChannelEncode
PlantRemoteSensor
dd
r
ControlChannel
SC
u
CC
log S d
log S d
benefits
feedback
SC
CCstabilize
remotesensing
remote control
log( )a
costs
Disturbance-e=d-u
ControlSensor
ChannelEncode
PlantRemoteSensor
dd
r
ControlChannel SC
u
CC
Bode/Shannon is likely a better p-to-p comms theory to serve as a foundation for networks than either Bode or Shannon alone.
log S d
log S d
SC
CClog( )a
Disturbance-e=d-u
ControlSensor
ChannelEncode
PlantRemoteSensor
dd
r
ControlChannel SC
u
CC
log S d
log S d
SC
CClog( )a
http://www.glue.umd.edu/~nmartins/
Nuno C Martins and Munther A Dahleh, Feedback Control in the Presence of Noisy Channels: “Bode-Like” Fundamental Limitations of Performance.(Submitted to the IEEE Transactions on Automatic Control) Abridged version in ACC 2005Fundamental Limitations of Disturbance Attenuation in the Presence of Side InformationNuno C. Martins, Munther A. Dahleh and John C. Doyle(Submitted to the IEEE Transactions on Automatic Control) Abridged version in CDC 2005
Work in progress
• Thermodynamics (Carnot)
• Communications (Shannon)
• Control (Bode)
• Computation (Turing/Gödel)
• We need a more integrated view and have
the beginnings
The search for simplicity
Question
AnswerSimple
Simple Simplicity
• Seek the hidden simplicity in our apparently complex world
The end of certainty
Question
AnswerSimple
Simple,
PredictableSimplicity
Complex,
Unpredictable
Emergence
1 0
1bounded 1 , =
2k k kc z cz z z
“Emergent” complexity
• Simple question• Undecidable
• No short proof• Chaos• Fractals
Mandelbrot
The complete picture
Question
AnswerSimple
Simple Simplicity
ComplexEmergence
Simple, predictable, reliable, robust versus
Complex, unpredictable, fragile
The complete picture
Question
AnswerSimple Complex
Simple Simplicity Organization
ComplexEmergence
Irreducibility
Simple, predictable, reliable, robust versus
Complex, unpredictable, fragile
The complete picture
Question
AnswerSimple Complex
Simple Simplicity Organization
ComplexEmergence
Irreducibility
Simple, predictable, reliable, robust versus
Complex, unpredictable, fragile
Nightmare
1 1k k kz cz z
But simulation is fundamentally limited• Gridding is not scalable• Finite simulation inconclusive
0
1
2z
The way forward?
Question
AnswerSimple
Simple,
PredictableSimplicity
Complex,
Unpredictable
Emergence
It’s easy to prove that this disk is in M.
Other points in M are fragile to the definition of the map.
1 1 k k kz c z z
Merely stating the obvious.
Main idea
Main idea
The longer the proof, the more fragile the remaining regions.
The proof of this region is a bit longer.
Main idea
And so on…
Proof even longer.
Easy to prove these points are in Mset.
Easy to prove these points are not in Mset.
Proofs get harder.(But all still “easy.”)
What’s left gets more fragile.
What’s left gets more fragile.
Breaking hard problems
• SOSTOOLS proof theory and software (Parrilo, Prajna, Papachristodoulou)
• Nested family of (dual) proof algorithms• Each family is polynomial time• Recovers many “gold standard” algorithms
as special cases, and immediately improves• No a priori polynomial bound on depth
(otherwise P=NP=coNP)• Conjecture: Complexity implies fragility
c “Robust yet
fragile” systems?
• Long proofs indicate fragility:• True fragility (which must be fixed) or• Artifact of the model (which must be rectified) or• Weakness in the proof techniques (ditto)
• Potentially fundamentally changes the apparent intractability of organized complexity• Brings back together two research areas :
• Numerical analysis and ill-conditioning• Computational complexity (P, NP/coNP, undecidable)
The way forward?
Question
AnswerSimple
Simple,
PredictableSimplicity
Complex,
FragileEmergence
Variety ofresponses
Variety of Ligands & Receptors
G-proteinsPrimary targets
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
• Ubiquitous protocol• “Hourglass”• “Robust yet fragile”• Robust & evolvable• Fragile to “hijacking”• Manages extreme
heterogeneity with selected homogeneity
• Accident or necessity?
Signal transduction
Random walk
Ligand Motion Motor
Bacterial chemotaxis
Ligand
SignalTransduction
gradient
Biased random walk
Motion Motor
CheY
PMF
++
++
From Taylor, Zhulin, Johnson
Variety of receptors/
ligands Common cytoplasmic
domains
Common signal carrier
Common energy carrier
Mot
or
Var
iety
of
Lig
ands
& R
ecep
tors
Tra
nsm
itte
r R
ecei
ver
Motor
Variety of Ligands& Receptors
Transmitter Receiver
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
50 such “two component” systems in E. Coli
• All use the same protocol- Histidine autokinase
transmitter- Aspartyl phospho-
acceptor receiver• Huge variety of receptors
and responses
Signal transduction
IP
TCP
Applications
Link
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
Motor
Variety of Ligands& Receptors
Transmitter Receiver
Motor
Variety of Ligands& Receptors
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
OtherResponses
Other Receptors &
Ligands
Transmitter Receiver
50 such “two component” systems in E. Coli
• All use the same protocol- Histidine autokinase
transmitter- Aspartyl phospho-
acceptor receiver• Huge variety of receptors
and responses
Molecular phylogenies show evolvability of the bowtie architecture.
invariant active-site residues
conserved residues of interaction surface with phosphotransferase domains
highly variable amino acids of the interaction surface
that are responsible for specificity of the
interaction
conserved functional domains
highly variability for
specificity of the interaction
• Automobiles: Keys provide specificity but no other function. Other function conserved, driver/vehicle interface protocol is “universal.”• Ethernet cables: Specificity via MAC addresses, function via standardized protocols.
MAC
invariant active-site residues
Variety ofresponses
Variety of Ligands & Receptors
G-proteinsPrimary targets
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
50 such “two component” systems in E. Coli
• Ubiquitous eukaryote protocol
• Huge variety of – receptors/ligands– downstream responses
• Large number of G-proteins grouped into similar classes
• Handful of primary targets
GGGDP GTP
GDPGTP
In
In
Out
P
RhoRhoGDP GTP
GDPGTP
In
In
Out
P
Ligand
Receptor
RGS
GDI
GDI
GEF
GAP
Responses
Receptors
G-proteinsPrimary targets
GDP GTP
GDPGTP
In
In
Out
P
P
GDP GTP
GDPGTP
Out
In
InSignal integration: High “fan in” and “fan out”
RobustnessImpedance matching:Independent of G of inputs and outputs
Speed, adaptation, integration, evolvability
UncertainUncertain
UncertainUncertain
Variety ofresponses
Variety of Ligands& Receptors
IntermediatesFragile
and hard to change
Robust and highly
evolvable
RhoRhoGDP GTP
GDPGTPIn
In
Out
P
GDI
GDI
GEF
GAP
Fragility?
• A huge variety of pathogens attack and hijack GTPases.
• A huge variety of cancers are associated with altered (hijacked) GTPase pathways.
• The GTPases may be the least evolvable elements in signaling pathways, in part because they facilitate evolvability elsewhere
GGGDP GTP
GDPGTP
In
C-AMP
P
Ligand
Receptor
Cholera toxin
Hijacking
Cholera toxins hijack the signal transduction by blocking a GTPase activity.
Bacterial virulence factors targeting Rho GTPases: parasitism or symbiosis?
Patrice Boquet and Emmanuel Lemichez
TRENDS in Cell Biology May 2003
Variety ofresponses
Variety of Ligands & Receptors
G-proteinsPrimary targets
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
• Ubiquitous protocol• “Hourglass”• Robust & evolvable• Fragile to “hijacking”• Manages extreme
heterogeneity with selected homogeneity
• Accident or necessity?
But you already knew all this.
A look back and forward
• The Internet architecture was designed without a theory.
• Many academic theorists told the engineers it would never work.
• We now have a nascent theory that confirms that the engineers were right.
• For MANETs and other new technologies, let’s hope we can avoid a repeat of this history.
MultiscalePhysics
SystemsBiology
Network Centric,Pervasive,Embedded,Ubiquitous
Core theory
challenges
RNAP
DnaK
RNAP
DnaK
rpoH
FtsH Lon
Heat
mRNA
DnaK
DnaK
ftsH
Lon
Regulation of
protein levels
Regulation of protein action
PMF
++
++
From Taylor, Zhulin, Johnson
Co-factors
Fatty acids
Sugars
NucleotidesAmino Acids
Catabolism
Proteins
Prec
urso
rs
Autocatalytic feedback Taxis and transport
Carriers
Core metabolism
Genes
Regulation & control
Trans*
Lessons from the cell and Internet
TCA
Gly
G1P
G6P
F6P
F1-6BP
PEP Pyr
Gly3p
13BPG
3PG
2PG
ATP
NADH
Oxa
Cit
ACA
Variety ofresponses
Variety of Ligands & Receptors
G-proteinsPrimary targets
Variety ofresponses
Variety of Ligands & Receptors
Transmitter
Receiver
• Ubiquitous protocol• “Hourglass”• “Robust yet fragile”• Robust & evolvable• Fragile to “hijacking”• Manages extreme
heterogeneity with selected homogeneity
• Accident or necessity?
Signal transduction
Allosteric regulation
Regulation of enzyme levels
Bio: Huge variety of environments, metabolisms
Huge variety of components
Huge variety of genomes
Universal control
architecture