http://doyle/shortcourse.htm systems biology shortcourse may 21-24 winnett lounge, caltech speakers:...

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.

http://www.cds.caltech.edu/~doyle/shortcourse.htm

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

Transport

Polymerization and

assemblyCore

metabolism

Autocatalytic and regulatory feedback

Whole cell metabolism

Met

abol

ite

En

zym

e

+Regulation

Au

toca

taly

sis

Metab

oliteE

nzym

e

kx

kx 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 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

nutrient flux

internal flux

product flux

Mass &Reaction

Energyflux

Balance

n

m

p

n

m v

p

S

S

S

Stoichiometry or mass and energy balance

metabolites

reactions

n

m

p

S

S

S

Nutrients Products

Internal

Core metabolism

Transport

Polymerization and

assemblyCore

metabolism



transport

Polymerization and

assemblyCore

metabolism


Nested “bowties”

Polymerization and

assembly

transport

Core metabolism

Nested “bowties”

Our first universal architecture

The core metabolism “bowtie”

Nutrients Products

Energy and reducing

Fatty acids

Sugars

Nucleotides

Amino Acids

Biosynthesis

Catabolism

Carriers and PrecursorMetabolites

Cartoon metabolism

Catabolism

Carriers and

PrecursorMetabolites

The metabolism “bowtie” protocol

Energy and reducing

Fatty acids

SugarsNucleotides

Amino Acids

Nutrients Products

CatabolismSynthesis

Core: special purpose enzymes controlled by competitive inhibition and allostery

Edges: general purpose polymerases and machines controlled by regulated recruitment

Uncertain Unce

rtain

Core: Highly efficient

Edges: Robustness and flexibility

Uncertain Unce

rtain

Almost everything complex is made this way:Cars, planes, buildings, power, fuel, laptops,…

This “cartoon” is pure protocol.

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


Variety of producers

Electric power

Variety ofconsumers

Electric power

Variety ofconsumers


Energy carriers

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

Complexassembly

Raw materials

Complexassembly

Raw materials

Buildingblocks

Collect and

import raw

materials

Common currencies

and building blocks

Complex assembly

Steel manufacturing

Variety ofconsumers


Energy carriers

transport assemblymetabolism

Core: special purpose machines controlled by allostery

Variety ofconsumers


Energy carriers


Edges: general purpose machines controlled by regulated recruitment

Variety ofconsumers


Energy carriers


Robust and evolvable

Variety ofconsumers


Energy carriers


Fragile and hard to change

Variety ofconsumers


Energy carriers


Preserved by selection on three levels:1. Fragile to change (short term)2. Facilitates robustness elsewhere (short term)3. Facilitates evolution (long term)

Modules and protocols

• Much confusion surrounds these terms

• Biologists already understand the important distinction

• Most of basic sciences doesn’t

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

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.

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

Varied components

Variedsystems

RobustMesoscale

The LEGO

connectorprotocol

http://www.lego.com/

Software

Hardware

Early computing

Analogsubstrate

Variousfunctionality

Digital

Software

Hardware

Hardware

Applications

OperatingSystem

ModernComputing

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”

Reductionism and protocols

• Reductionism = modules are more important than protocols

• Usually: “Huh? What’s a protocol?”

• Systems approach: Protocols are as important as modules

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.

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.

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

Taxis and transport

Polymerization and

assembly

Core metabolism


Nested bowtie and hourglass

Enzymes are modules.Enzymes are modules.

“Bowtie architectures” is a protocol.


Conservation of energy and moiety is a law.


essential: 230 nonessential: 2373 unknown: 1804 total: 4407

http://www.shigen.nig.ac.jp/ecoli/pec

Autocatalytic feedback

Regulatory feedback



Regulatory feedback


Knockouts often lose robustness, not minimal functionality


Knockouts often lethal


Brakes

Airbags

Seatbelts

MirrorsWipers

Headlights

Steering

GPS Radio

ShiftingTraction control

Anti-skid

Electronic ignition

Electronic fuel injectionTemperature control

Cruise control

Bumpers Fenders

Suspension (control)

Seats


Regulatory feedback

transport assemblymetabolismSupplies

Materials &Energy

SuppliesMaterials &

Energy

SuppliesRobustness

SuppliesRobustness

Robustness

Complexity

Robustness

Complexity


Regulatory feedback


If feedback regulation is the dominant protocol, what are the laws constraining what’s possible?

If feedback regulation is the dominant protocol, what are the laws constraining what’s possible?


Regulatory feedback


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?

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?


Regulatory feedback

transport assemblymetabolismSupplies

Materials &Energy

SuppliesMaterials &

Energy

SuppliesRobustness

SuppliesRobustness

What are the laws of robustness?What are the laws of robustness?

Transport

Polymerization and

assemblyCore

metabolism



Met

abol

ite

En

zym

e

+Regulation

Au

toca

taly

sis

Metab

oliteE

nzym

e

kx

kx 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 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

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 1 1 0

max0

( ) ( )

( )

( )1

n

d

fb

x V x V x

VV x t

hx t t

K

perturbation

Product inhibitionYi, Ingalls, Goncalves, Sauro

nx

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

hx t t

K

Step increase in demand for “ATP.”

h = 3

0 5 10 15 20Time

h = 3

h = 2

h = 1

h = 0

Tighter steady-stateregulation

Transients, Oscillations

Higher feedback “gain”

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(



log )nx d constant F(

Theorem


log|S |

Tighter regulation


Biological complexity is dominated by the evolution of

mechanisms to more finely tune this robustness/fragility tradeoff.

This tradeoff is a law.

kx

1 1( )k kV x

( )k kV xProduct inhibition is a protocol.

log|S |

This tradeoff is a law.

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.


log|S |

Define log "fragility" ( )nS S x F

Conservation of “fragility”

Robust

Fragile

Uncertainty

Diseases of complexity

ParasitesCancer

EpidemicsAuto-immune diseaseComplex development

Regeneration/renewalComplex societiesImmune response

Xn+1

X0 X1 Xi Xn… … Error

log|S |

We have a proof of this.

Robust

Fragile

Uncertainty

ParasitesCancer

EpidemicsAuto-immune diseaseComplex development

Regeneration/renewalComplex societiesImmune response

This is a cartoon. We have no proof of this. Yet.

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?

0 5 10 15 20Time

h = 3

h = 2

h = 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

Lo

g(S

n/S

0)

h = 3

h = 2

h = 1

h = 0




Metabolite

Enzyme

+RegulationAutocatalysis

0log ( )

log logk k

S d

Energy and

materials


Regulatory feedback


Even though autocatalytic feedback contributes relatively modestly to complexity, it has a huge indirect impact on regulatory complexity.

Even though autocatalytic feedback contributes relatively modestly to complexity, it has a huge indirect impact on regulatory complexity.


Regulatory feedback


• 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…

• 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…

0 5 10 15 20Time

h = 3

h = 2

h = 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

Lo

g(S

n/S

0)

h = 3

h = 2

h = 1

h = 0



Regulatory feedback

only

Add autocatalytic feedback

more

log )

Increases

nx d F(

Add autocatalytic

feedback


log )

0

nx d

constant

F(

Add more regulator feedback

More “instability” aggravates

0log ( ) logS d

Increase log

Control demo

0log ( ) log 1/S d L

L


Regulatory feedback


Enzymes are modules.

Enzymes are modules.

“Bowtie architectures” with product inhibition

is a protocol suite.

“Bowtie architectures” with product inhibition

is a protocol suite.

Conservation of energy,

moiety, and fragility are

laws.

Conservation of energy,

moiety, and fragility are

laws.

Taxis and transport

Polymerization and

assembly

Core metabolism


Nested bowtie and hourglass

Enzymes are modules.Enzymes are modules.





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.

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.

A coherent foundation for a general understanding

of highly evolved complexity

Recently, there has been a remarkable convergences.

Molecular biology has catalogued cellular components, and network

structure is becoming more apparent.

Biology

Advanced technologies are producing networks approaching biological levels of complexity (which is hidden to the user).

BiologyAdvanced

Technology

BiologyAdvanced

TechnologyMath

New mathematics provides for the first time a coherent theoretical

framework for complex networks (but not yet an accessible one).

A coherent foundation for a general understanding

of highly evolved complexity

BiologyAdvanced

TechnologyMath

After many false starts.

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

• 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

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

NP

coNP

P“easy”

Hard Problems

NP

coNP

P

Controls

Communications

Economics

Dynamical Systems

Physics

Algorithms

Hard Problems

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

NP

coNP

P

Controls

Communications

Economics

Dynamical Systems

Physics

Algorithms

Hard ProblemsInternet

NP

coNP

P

Controls

Communications

Economics

Dynamical Systems

Physics

Algorithms

Hard ProblemsBiology

Internet

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

NP

coNP

P

Hard Problems

Controls

Communications

Economics

Dynamical Systems

Physics

Algorithms

Goal

Goal Internet

Biology

UnifiedTheory

NP

coNP

P

Controls

Communications

Economics

Dynamical Systems

Physics

Algorithms

Hard ProblemsBiology

Internet

http://doyle/shortcourse.htm systems biology shortcourse may 21-24 winnett lounge, caltech speakers:...

Documents

core metabolism slide

blocks slide

allostery slide

evolvable slide

cell metabolism slide

regulatory feedback

regulation autocatalysis

regulated recruitment