cdsltech/~doyle/shortcourse.htm Systems Biology Shortcourse May 21-24
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Transcript of cdsltech/~doyle/shortcourse.htm Systems Biology Shortcourse May 21-24
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http://www.cds.caltech.edu/~doyle/shortcourse.htm
Systems Biology Shortcourse May 21-24
Winnett Lounge, Caltech
Speakers: Adam Arkin (UC Berkeley), Frank Doyle (UCSB), Drew Endy (MIT), Dan Gillespie (Caltech), Michael Savageau (UC Davis)
Organized by John Doyle (Caltech). There is no registration or fees. Note: Friday 4pm talk by Adam Arkin in Beckman Institute Auditorium.
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Collaborators and contributors(partial list)
Theory: Parrilo, Carlson, Paganini, Papachristodoulo, Prajna, Goncalves, Fazel, Lall, D’Andrea, Jadbabaie, many current and former students, …
Web/Internet: Low, Willinger, Vinnicombe, Kelly, Zhu,Yu, Wang, Chandy, Effros, …
Biology: Csete,Yi, Tanaka, Arkin, Savageau, Simon, AfCS, Kurata, Khammash, El-Samad, Gross, Bolouri, Kitano, Hucka, Sauro, Finney, …
Turbulence: Bamieh, Dahleh, Bobba, Gharib, Marsden, …Physics: Mabuchi, Doherty, Barahona, Reynolds,
Asimakapoulos,…Engineering CAD: Ortiz, Murray, Schroder, Burdick, …Disturbance ecology: Moritz, Carlson, Robert, …Finance: Martinez, Primbs, Yamada, Giannelli,… Caltech faculty
Other Caltech
Other
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Transport
Polymerization and
assemblyCore
metabolism
Autocatalytic and regulatory feedback
Whole cell metabolism
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Met
abol
iteE
nzym
e
+Regulation
Aut
ocat
alys
is
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Met
abol
iteE
nzym
e
+Regulation
Aut
ocat
alys
is
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Metabolite
Enzym
e
kxkx dt ( )k kV xkx+1 1( )k kV x -
+
kx
1 1
max
( ) ( )
( )1
( )
k k k k k k
m
x V x V xVV x t
t
x
Kx
max( )1
( )m
VV x t Kx t
( )x t
1 1( )k kV x
1kx
( )k kV xkx
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nutrient fluxinternal fluxproduct flux
Mass &Reaction
Energyflux
Balance
n
m
p
nm vp
SSS
Stoichiometry or mass and energy balance
metabolites
reactions
n
m
p
SSS
Nutrients Products
Internal
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Core metabolism
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Transport
Polymerization and
assemblyCore
metabolism
Autocatalytic and regulatory feedback
Whole cell metabolism
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transport
Polymerization and
assemblyCore
metabolism
Autocatalytic and regulatory feedback
Nested “bowties”
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Polymerization and
assembly
transport
Core metabolism
Nested “bowties”
Our first universal architecture
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The core metabolism “bowtie”
Nutrients Products
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Energy and reducing
Fatty acids
Sugars
Nucleotides
Amino Acids
Biosynthesis
CatabolismCarriers and PrecursorMetabolites
Cartoon metabolism
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Catabolism
Carriers and
PrecursorMetabolites
The metabolism “bowtie” protocol
Energy and reducing
Fatty acids
SugarsNucleotides
Amino Acids
Nutrients Products
CatabolismSynthesis
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Core: special purpose enzymes controlled by competitive inhibition and allostery
Edges: general purpose polymerases and machines controlled by regulated recruitment
Uncertain Uncertai
n
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Core: Highly efficient
Edges: Robustness and flexibility
Uncertain Uncertai
n
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Almost everything complex is made this way:Cars, planes, buildings, power, fuel, laptops,…
This “cartoon” is pure protocol.
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Collect and
import raw
materials
Common currencies
and building blocks
Complex assembly
Manufacturing and metabolism
Collect and
import raw
materials
Common currencies
and building blocks
Complex assembly
Taxis and transport
Polymerization and assembly
Core metabolism
Autocatalytic and regulatory feedback
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Variety of producers
Electric power
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Variety ofconsumers
Electric power
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Variety ofconsumers
Variety of producers
Energy carriers
• 110 V, 60 Hz AC• (230V, 50 Hz AC)• Gasoline• ATP, glucose, etc• Proton motive force
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Complexassembly
Raw materials
Complexassembly
Raw materials
Buildingblocks
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Collect and
import raw
materials
Common currencies
and building blocks
Complex assembly
Steel manufacturing
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Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Core: special purpose machines controlled by allostery
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Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Edges: general purpose machines controlled by regulated recruitment
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Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Robust and evolvable
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Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Fragile and hard to change
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Variety ofconsumers
Variety of producers
Energy carriers
transport assemblymetabolism
Preserved by selection on three levels:1. Fragile to change (short term)2. Facilitates robustness elsewhere (short term)3. Facilitates evolution (long term)
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Modules and protocols
• Much confusion surrounds these terms• Biologists already understand the important
distinction• Most of basic sciences doesn’t
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Modules and protocols in experiments
• Modules: components of experiments • Protocols: rules or recipes by which the
modules interact • This generalizes to most important
situations• Important distinction in experiments• Even more important in understanding the
complexity of biological networks
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Modules and protocols example
• Suppose some specific experimental protocol has a step that requires the use of a PCR machine module.
• The PCR machine in turn implements a complex protocol with its own modules.
• Thus protocols and modules are hierarchically nested.
• A nested collection of protocols/modules is called an architecture or protocol suite.
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Modules and protocols example
• Consider this laptop/projector combination.• The modules include software, hardware,
and connectors.• The protocols are the rules by which these
modules must interact.• Hardware modules change between talks• Within talks slides change, not hardware• Robust and “evolvable” yet fragile
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Modules and protocols example
• Consider this laptop/projector combination.• The modules include software, hardware,
and connectors.• The protocols are the rules by which these
modules must interact.• Hardware modules change between talks• Within talks slides change, not hardware• Robust and “evolvable” yet fragile
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Software
Hardware
Early computing
Analogsubstrate
Variousfunctionality
Digital
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Software
Hardware
Hardware
Applications
OperatingSystem
ModernComputing
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Software
Hardware
Hardware
Applications
OperatingSystem
ModernComputing
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Modules and protocols
• Protocols and modules are complementary (dual) notions
• Primitive technologies = modules are more important than protocols
• Advanced technologies = protocols are at least as important
• Even bacteria are “advanced technology”
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Reductionism and protocols
• Reductionism = modules are more important than protocols
• Usually: “Huh? What’s a protocol?”
• Systems approach: Protocols are as important as modules
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Necessity or “frozen accident”?
• Laws are absolute necessity.• Conjecture: Protocols in biology are largely
necessary. (More so than in engineering!)• Modules??? Appear to be more of a mix of
necessity and accident.
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Necessity or “frozen accident”?
• Conservation laws are necessary.• Bowtie protocols are essentially necessary
if robustness and efficiency are required.• Conjecture: It is necessary that there is an
energy carrier, it may not be necessary that it be ATP.
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Conjectures on laws and protocols
• The important laws governing biological complexity have yet to be fully articulated
• Biology has highly organized dynamics using protocol suites
• Both are true for advanced technologies
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Taxis and transport
Polymerization and
assembly
Core metabolism
Autocatalytic and regulatory feedback
Nested bowtie and hourglass
Enzymes are modules.
“Bowtie architectures” is a protocol.
Conservation of energy and moiety is a law.
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essential: 230 nonessential: 2373 unknown: 1804 total: 4407
http://www.shigen.nig.ac.jp/ecoli/pec
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
Knockouts often lose robustness, not minimal functionality
Knockouts often lethal
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Brakes
Airbags
Seatbelts
MirrorsWipers
Headlights
Steering
GPS Radio
ShiftingTraction control
Anti-skid
Electronic ignition
Electronic fuel injectionTemperature control
Cruise control
Bumpers FendersSuspension (control)
Seats
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Brakes
Airbags
Seatbelts
MirrorsWipers
Headlights
Steering
GPS Radio
ShiftingTraction control
Anti-skid
Electronic ignition
Electronic fuel injectionTemperature control
Cruise control
Bumpers FendersSuspension (control)
Seats
Knockouts often lose robustness, not minimal functionality
Knockouts often lethal
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolismSupplies
Materials &Energy
SuppliesRobustness
Robustness
Complexity
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
If feedback regulation is the dominant protocol, what are the laws constraining what’s possible?
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
A historical aside:• These systems are not at the edge-of-chaos,
self-organized critical, scale-free, at an order-disorder transition, etc
• Not only are they as opposite from this as can possibly be (an observational fact)…
• But also, it is provably impossible for robust systems to have it otherwise (a theoretical assertion)
• The facts are easily checked, what is the theoretical foundation?
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolismSupplies
Materials &Energy
SuppliesRobustness
What are the laws of robustness?
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Transport
Polymerization and
assemblyCore
metabolism
Autocatalytic and regulatory feedback
Whole cell metabolism
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Met
abol
iteE
nzym
e
+Regulation
Aut
ocat
alys
is
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Met
abol
iteE
nzym
e
+Regulation
Aut
ocat
alys
is
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Metabolite
Enzym
e
kxkx dt ( )k kV xkx+1 1( )k kV x -
+
kx
1 1
max
( ) ( )
( )1
( )
k k k k k k
m
x V x V xVV x t
t
x
Kx
max( )1
( )m
VV x t Kx t
( )x t
1 1( )k kV x
1kx
( )k kV xkx
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kx
1 1
max
( ) ( )
( )1
( )
k k k k k
m
x V x V xVV x t Kx t
1 1( )k kV x
( )k kV x
nx
1 1 1 0
max0
( ) ( )
( )( )1
n
d
fb
x V x V xVV x t hx t tK
perturbation
Product inhibitionYi, Ingalls, Goncalves, Sauro
nx
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h = [0 1 2 3]
0 5 10 15 200.8
0.85
0.9
0.95
1
1.05
Time (minutes)
[ATP
]
h = 2h = 1
h = 0
1 1 1 0
max0
( ) ( )
( )( )1
n
d
fb
x V x V xVV x t hx t tK
Step increase in demand for “ATP.”
h = 3
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0 5 10 15 20Time
h = 3
h = 2h = 1
h = 0
Tighter steady-stateregulation
Transients, Oscillations
Higher feedback “gain”
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0 5 10 15 200.8
0.85
0.9
0.95
1
1.05
Time (minutes)
[ATP
]
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
Log(
Sn/S
0)
h = 3
h = 0
Spectrum
Time response
Robust
Yet fragile
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0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Log(
Sn/S
0)
h = 3
h = 0 Robust
Yet fragile
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0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Log(
Sn/S
0)
h = 0 Robust
Yet fragile
log ) ?nx d constant F(
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0 2 4 6 8 10-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Frequency
Log(
Sn/S
0)
h = 3
h = 2
h = 1
h = 0
log )nxF(
Tighter steady-stateregulation
Transients, Oscillations
log )nx d constant F(Theorem
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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.
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kx
1 1( )k kV x
( )k kV xProduct inhibition is a protocol.
log|S |
This tradeoff is a law.
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kx
1 1( )k kV x
( )k kV x
Product inhibition is a protocol.
log|S |
This tradeoff is a law. PFK and ATP are modules.
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log )nx d constant F(
log|S |
Define log "fragility" ( )nS S x F
Conservation of “fragility”
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Robust
Fragile
Uncertainty
Diseases of complexity
ParasitesCancer
EpidemicsAuto-immune diseaseComplex development
Regeneration/renewalComplex societiesImmune response
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Xn+1
X0 X1 Xi Xn… … Error
log|S |
We have a proof of this.
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Robust
Fragile
Uncertainty
ParasitesCancer
EpidemicsAuto-immune diseaseComplex development
Regeneration/renewalComplex societiesImmune response
This is a cartoon. We have no proof of this. Yet.
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Robust
Fragile
Uncertainty
ParasitesCancer
EpidemicsAuto-immune
disease
Immune responseDevelopmentRegenerationrenewalSocieties
Why should any biologists care about this?How does it effect what can be done to understand complex biological networks?
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0 5 10 15 20Time
h = 3
h = 2h = 1
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
Log(
Sn/S
0)
h = 3
h = 2
h = 1
h = 0
Tighter steady-stateregulation
Transients, Oscillations
log )nx d constant F(
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Metabolite
Enzyme
+RegulationAutocatalysis
0log ( )
log logk k
S d
Energy and
materials
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
Even though autocatalytic feedback contributes relatively modestly to complexity, it has a huge indirect impact on regulatory complexity.
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
• Autocatalysis is everywhere in human and natural systems as well as biology
• Make energy, materials, and machines to make energy, materials, and machines to make …
• Consumers are investors are labor…
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0 5 10 15 20Time
h = 3
h = 2h = 1
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
Log(
Sn/S
0)
h = 3
h = 2
h = 1
h = 0
Tighter steady-stateregulation
Transients, Oscillations
Regulatory feedback
only
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Add autocatalytic feedback
more
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log )
Increasesnx d F(
Add autocatalytic
feedback
Transients, Oscillations
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log )
0nx d
constant
F(
Add more regulator feedback
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More “instability” aggravates
0log ( ) logS d
Increase log
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Control demo
0log ( ) log 1/S d L
L
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Autocatalytic feedback
Regulatory feedback
transport assemblymetabolism
Enzymes are modules.
“Bowtie architectures” with product inhibition
is a protocol suite.
Conservation of energy,
moiety, and fragility are
laws.
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Taxis and transport
Polymerization and
assembly
Core metabolism
Autocatalytic and regulatory feedback
Nested bowtie and hourglass
Enzymes are modules.
“Bowtie architectures” is a protocol.
Conservation of energy and moiety is a law.
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Key themes
1. Multiscale and large-scale stochastic simulation is an essential technology for systems biology.
2. Simulation alone is not scalable to larger network problems because complex, uncertain systems need an exponentially large number of simulations to answer biologically meaningful questions.
3. There are fundamental laws governing the organization of biological networks.
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Hypotheses 1. Multiscale and large-scale stochastic simulation.
Gillespie + Petzold for stiff stochastic systems.
2. Simulation alone is not scalable. Automated scalable inference using SOSTOOLS.
3. There are fundamental laws governing the organization of biological networks. Without exploiting these, the complexity is overwhelming.
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A coherent foundation for a general understanding
of highly evolved complexity
Recently, there has been a remarkable convergences.
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Molecular biology has catalogued cellular components, and network
structure is becoming more apparent.
Biology
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Advanced technologies are producing networks approaching biological levels of complexity (which is hidden to the user).
BiologyAdvanced
Technology
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BiologyAdvanced
TechnologyMath
New mathematics provides for the first time a coherent theoretical
framework for complex networks (but not yet an accessible one).
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A coherent foundation for a general understanding
of highly evolved complexity
BiologyAdvanced
TechnologyMath
After many false starts.
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Complementary ways to tell this story:1. Give lots of examples from biology and
technology2. Prove relevant theorems 3. Deliver useful software tools
BiologyAdvanced
TechnologyMath
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• Today: an attempt to distill an accessible message from enormous amount of detail
• Focus on universal laws that transcend details• Minimize math, maximize examples• Provide broader context for the rest of the
shortcourse
BiologyAdvanced
TechnologyMath
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This week:• Case studies in microbial signaling and
regulation networks• Will attempt to put details into broader context• Saturday will consider computational
challenges
BiologyAdvanced
TechnologyMath
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NP
coNP
P“easy”
Hard Problems
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NP
coNP
P
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
Hard Problems
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P
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
• Domain-specific assumptions• Enormously successful
• Handcrafted theories• Incompatible assumptions
• Tower of Babel where even experts cannot communicate• “Unified theories” failed• New challenges unmet
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NP
coNP
P
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
Hard ProblemsInternet
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NP
coNP
P
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
Hard ProblemsBiology
Internet
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Biology and advanced technology• Biology
– Integrates control, communications, computing– Into distributed control systems– Built at the molecular level
• Advanced technologies will increasingly 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
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NP
coNP
P
Hard Problems
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
Goal
Goal Internet
Biology
UnifiedTheory
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NP
coNP
P
Controls
Communications
Economics
Dynamical Systems
Physics
Algorithms
Hard ProblemsBiology
Internet