生物信息資源 林標(biāo)揚(yáng)-systemsbiology學(xué)習(xí)資料

Systems

Biology-IntroductionBiaoyang

Lin林標(biāo)揚(yáng)but

is

this

leading

to

increased

under-standing

of

the

nature

of

life?

Do

we,

in

fact,

understand

life

any

better

than

at

the

time

of

Erwin

Schr?dinger*

in

1944?E.

Schr?dinger

(1944)

What

is

life?

Cambridge

UniversityPressWe

are

living

through

a

period

in

which

the

main

activity

in

biological

research

is

the

accumulation

of

more

and

more

facts,Limitations

of

biochemistry

and

molecularbiologyThe

‘omes’GenomeTranscriptomeProteomeMetabolomeTranscriptome:

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CATPWhat

Is

Systems

Biology??

Biology?went?top-down?for?the?last?50?years?–

From?cell?to?protein?to?gene?..?–

Huge?amounts?of?data?produced??Challenge:?put?the?pieces?back?together?again??

Attempts?to?create?predictive?models?of?cells,?organs,?biochemical?processes?and?complete?organisms?–?

Data

combined

with

computational,

mathematical

andengineering

disciplines–?

Model

<->

simulations

<->

experimentDefinition

of

Systems

BiologySystems

Biology系

的概念??

系

生物學(xué)中系

的概念或整體的概念

或哲學(xué)觀，最早可以追溯到公元前300

年的

里士多德(Aristotle)。整體哲學(xué)觀是指一個(gè)整體可以被人為地分為不同的組分，但是這個(gè)整體的特性并不能從這些組分中所含有的知識完全對它進(jìn)行解釋。整體的哲學(xué)觀在中國古代的《易

》和傳

中醫(yī)學(xué)中也有詳細(xì)的記載和體現(xiàn)。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社??

系

生物學(xué)的第二個(gè)起源可以追溯到18世紀(jì)中晚期，

生理學(xué)之父

--ClaudeBernard提出的

體內(nèi)恒定理論

(Homeostasis)。該理論是指一個(gè)生命有機(jī)體需要很多

態(tài)的、平衡的調(diào)節(jié)(包括正反饋和負(fù)反饋等)，來維持其內(nèi)環(huán)境達(dá)到一個(gè)穩(wěn)定的、恒定的狀態(tài)。系

的概念摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社??

系

生物學(xué)中系

概念的第三個(gè)起源可以追

溯到20世紀(jì)50年代，Nobert

Wiener提出的

控制論

和

Ludwig

von

Bertalanffy

提出的一般系

理論

。而系

生物學(xué)真正的起源是在20世紀(jì)90年代后期，人類基因組的完成以及高通量技術(shù)的產(chǎn)生，如DNA芯片技術(shù)、高通量蛋白質(zhì)組學(xué)技術(shù)等的

展，使系

生物學(xué)真正

現(xiàn)

展。同時(shí)，計(jì)算科學(xué)計(jì)算能力的不斷提高，也促進(jìn)了系

生物學(xué)的

展。系

的概念摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社一般系

理論

(generalsystems

theory)??

CHAOS理論(混沌理論)；??

元胞自

機(jī)、細(xì)胞自

機(jī)

(cellularautomata)；??

突

論或?yàn)?zāi)

論(catastrophe)；??

等級層次理論

（hierachical

system)。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社混沌理論(Chaos

theory)??

混沌理論(Chaos

theory)是由美國氣象學(xué)家E.N.洛倫

茨(Lorenz)在20世紀(jì)60年代初研究天氣預(yù)報(bào)中大氣流

問題時(shí)首先

現(xiàn)的。他在計(jì)算機(jī)上模

地球大氣的研究中

現(xiàn)，只要計(jì)算機(jī)模

出

點(diǎn)的初始值有一個(gè)很微小的差異(小數(shù)點(diǎn)后第3位數(shù))，模

的結(jié)果就截然不同。由于在技術(shù)上不可能以無限精度測量初始值，因此我們不可能預(yù)言任何混沌系

(在這里指

期天氣預(yù)報(bào))的最后結(jié)果。但是，洛倫茨還現(xiàn)，混沌系

盡管看起來雜亂無章，但其

具有某種規(guī)律(patterns)。對混沌系

的模

，計(jì)算機(jī)可

出幾千個(gè)可能的預(yù)測，這些預(yù)測在某種狀態(tài)范圍內(nèi)是隨機(jī)分布的，但也有一定的模式。正如每日的天氣可以

化多端，不可對它進(jìn)行

期的預(yù)測，但逐年的氣候還是保持某種穩(wěn)定性的。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社??

1972年，洛倫茨做題為

Predictability:

Does

the

Flap

of

a

Butterfly’s

Wings

in

Brazil

set

off

a

Tornado

in

Texas?”(預(yù)測性：是否巴西蝴蝶的一個(gè)偶然的扇

將會在德克薩斯州制造一次

卷

？)的會議報(bào)告，也說明氣候的

化這個(gè)復(fù)雜系對起始的條件是非常敏感的?；煦缋碚?Chaos

theory)摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社一般系

理論

(generalsystems

theory)??

CHAOS理論(混沌理論)；??

元胞自

機(jī)、細(xì)胞自

機(jī)

(cellularautomata)；??

突

論或?yàn)?zāi)

論(catastrophe)；??

等級層次理論

（hierachical

system)。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社一般系

理論

(generalsystems

theory)??

CHAOS理論(混沌理論)；??

元胞自

機(jī)、細(xì)胞自

機(jī)

(cellularautomata)；??

突

論或?yàn)?zāi)

論(catastrophe)；??

等級層次理論

（hierachical

system)。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社

災(zāi)

論(catastrophe

theory)??

災(zāi)

論(catastrophe

theory),或稱突

論，是指在非線性系

中，

某些參數(shù)的微小

化，就可使整個(gè)系

失去平衡，使系

生重

大的、突然的

化。??

在20世紀(jì)60年代末，災(zāi)

論是由法國數(shù)學(xué)家R.托姆(René

Thom)

為解釋胚胎學(xué)的成胚過程而提出來的(Thom，1972)。70年代

以后，E.C.塞曼(Christopher

Zeeman)等人進(jìn)一步

展了災(zāi)

論

，并把它應(yīng)用到生物學(xué)、生態(tài)學(xué)、醫(yī)學(xué)、

學(xué)等領(lǐng)域。災(zāi)

論研

究跳

式轉(zhuǎn)

、不連續(xù)過程和突

的質(zhì)

。災(zāi)

論建立在結(jié)構(gòu)穩(wěn)

定性的基礎(chǔ)上。結(jié)構(gòu)穩(wěn)定性反映同一物種在千差萬

形態(tài)中的

相似性。穩(wěn)定結(jié)構(gòu)的喪失，就是突

的開始。災(zāi)

論是研究不連續(xù)現(xiàn)象的一個(gè)新數(shù)學(xué)分支，也是一般形態(tài)學(xué)的一種理論，能為

自然界中形態(tài)的

生和演化提供數(shù)學(xué)模型。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社一般系

理論

(generalsystems

theory)??

CHAOS理論(混沌理論)；??

元胞自

機(jī)、細(xì)胞自

機(jī)

(cellularautomata)；??

突

論或?yàn)?zāi)

論(catastrophe)；??

等級層次理論

（hierachical

system)。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社等級層次理論(Hierarchy

Theory)??

等級和層次普遍存在于我們的社會、生物系

以及生物分類等。等

級層次理論(Hierarchy

Theory)就是從數(shù)學(xué)角度把一個(gè)系

分成

有等級、有層次的不同部分(Pattee，1973)。在不同等級

，有一

定的非對稱關(guān)系(asymmetric

relationships)，這種非對稱關(guān)系是指上一層的等級高于下一層的等級，并且每一等級與上面層次的關(guān)系和與下面層次的關(guān)系是不對稱的；從生物學(xué)角度來說，也就是更高一層次的功能并不能在另外一個(gè)層次上被還原。根據(jù)等級層次理論，一個(gè)系

的復(fù)雜性(Complexity)與復(fù)合性(complicatedness)是不同的：若一個(gè)等級系

由許多低水平的層次所構(gòu)成，并且有相當(dāng)簡單的組

結(jié)構(gòu)，這種層次不豐富的等級結(jié)構(gòu)不屬于復(fù)雜(complex)系

，而是被認(rèn)為是復(fù)合(complicated)系

。即假如一個(gè)很大系

的組

結(jié)構(gòu)非常簡單，則綜合在一起的行為還是比較簡單的。反之，假如一個(gè)復(fù)合系

的結(jié)構(gòu)比較復(fù)雜，則其行為也會比較復(fù)雜。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社History??

Term

coined

at

1960s,

however

theoretical

people

and

experimental

biologists

diverged??

Renaissance

at

1990s–?Biology

becoming

cross-disciplinary,information

based,

high

throughput

scienceprotein-inhibitorbinding

constantsThe

systems

biology

agenda

Genome-wideprotein-metabolite

binding

constants

Genome-wide

high-throughput

enzyme

kinetics

Genome-wide

protein-proteinbinding

constants

Transcriptome

Proteome

MetabolomeRegulatory

interactions

Model

organism/

system

of

choiceExperimentationAnalysisNew

theoryNew

methodologyGenome-wide

Database,

schema

standards(Chemical

genetics)

Modelling;

ODEs,

Constraint-based

optimisation,

Solving

inverse

problems,

Novel

strategiesIteration

between

theory

and

experiment

Over-

&

Underlying

theories

KNOWLEDGE/

HYPOTHESISINDUCTIONDEDUCTIONOBSERVATIONS/

DATAINDUCTIONINDUCTIONDEDUCTIONDEDUCTIONDEDUCTIONDEDUCTION

Knowledge/Ideas

by

hypothesis

Knowledge/Ideas

by

hypothesisKnowledge/OBSERVATIONS/

DATA

Underlying

theory(Physics,

Chemistry)OBSERVATIONS/

DATAIdeas

by

hypothesis

INDUCTION

OBSERVATIONS/

DATASystems

Biology

has

variousmodes??

Top

down

versus

bottom-up;

analytic

versus

synthetic;

data

driven

versus

hypothesis

driven??

Historical:

molecular

biology

versusmathematical

biologyThe

goals

of

Systems

BiologySystems

Biology

is:Carnap

(Philosophical

Foundationsof

Physics

(1966))?Philosophy

of

Systems

BiologyThomas

Kuhn:

ParadigmstruggleKey

features

of

biologicalsystemsEmergentRobustness

Complexity

ModularityEmergent

PropertiesEmergent

properties

系

涌現(xiàn)性??

系

涌現(xiàn)性(emergence)是指一個(gè)系

自

形成一些新的系

特性，這些特性不能從其組成部分的特性中預(yù)測出來。因此，系

涌現(xiàn)性有三個(gè)重要的特征：①原來并不存在的特征；②新的、

可定性的，新涌現(xiàn)的特性具有質(zhì)的突

；③不能從其組成部分的

特性中預(yù)測，所以系

涌現(xiàn)性有

于系

的預(yù)測性。系

的預(yù)測

性(anticipation)是指系

可被預(yù)測的一些特性。如某些系

的組

成部分、特征以及環(huán)境的相互作用有一定的規(guī)律性，給出一定的

參數(shù)后，即可預(yù)測系

的特性。此時(shí)，即使產(chǎn)生新的系

特征，也是可被預(yù)測的，有于系

涌現(xiàn)性所產(chǎn)生的特征。??

摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社

Robustness

in

SimpleBiochemical

Networks–?–?Barkai

N,

Leibler

S.Nature

1997

Jun

26;387(6636):913-7“The

complexity

of

biochemical

networks

raises

the

question

ofthe

stability

of

their

functioning…The

key

properties

of

biochemical

networks

are

robust:

relativelyinsensitive

to

the

precise

values

of

biochemical

parameters.

“Papers

on

Robustness–?–?–?–?–?Experimental

support:

Robustness

in

bacterial

chemotaxisAlon

U,

Surette

MG,

Barkai

N,

Leibler

S.

Nature

(1999)Establishment

of

developmental

precision

and

proportions

in

theearly

Drosophila

embryo.

Houchmandzadeh

B,

Wieschaus

E,

Leibler

S.Nature

(2002)Robustness

of

the

BMP

morphogen

gradient

in

Drosophilaembryonic

patterning.

Eldar

A,

Dorfman

R,

Weiss

D,

Ashe

H,

Shilo

BZ,

BarkaiN.

Nature

(2002)Physical

properties

determining

self-organization

of

motors

andmicrotubules.Surrey

T,

Nedelec

F,

Leibler

S,

Karsenti

E.

Science

2001Integrated

genomic

and

proteomic

analyses

of

a

systematicallyperturbed

metabolic

network.Ideker

T,

Thorsson

V,

Ranish

JA,

Christmas

R,

Buhler

J,

Eng

JK,

Bumgarner

R,Goodlett

DR,

Aebersold

R,

Hood

L

Science

2001

穩(wěn)健性??

生物系

都是

態(tài)的系

。

態(tài)系

理論中，一個(gè)很重要的概念就

是系

狀態(tài)

(system

state)。系

狀態(tài)是指用某一時(shí)點(diǎn)的足

多

的信息來預(yù)測未來系

行為的系

描述，常用一組

量來表示。

如在代謝物網(wǎng)絡(luò)的微分方程模型中，系

狀態(tài)就是每一種化學(xué)

物質(zhì)濃度的集合；在隨機(jī)模型中，系

狀態(tài)是一個(gè)概率分布或者

每種生物分子數(shù)的集合。一個(gè)系

的穩(wěn)定態(tài)(steady

state)，或稱

穩(wěn)定狀態(tài)(stationary

state)或不

點(diǎn)(fixed

point),

指的是在時(shí)

上所有系

量的值都保持相對不

的狀態(tài)。??

生物系

的穩(wěn)健性是指生物系

能抵抗內(nèi)部和外部干擾，并維持其功能的一種特性(Kitano

2004;

Kitano

2007)。理解生物系

的穩(wěn)健性是深刻理解生命現(xiàn)象的一個(gè)基礎(chǔ)。生物系

的穩(wěn)健性基本可以體現(xiàn)在以下三個(gè)方面。①適應(yīng)性

(adaptation)：即生物體對環(huán)境條件

化的適應(yīng)；②不敏感性(parameter

insensitivity)：即系對某些

態(tài)參數(shù)是相對不敏感的；③逐漸地降解性(graceful

degradation)：指在一般的條件下，單個(gè)系

的功能受到損害后，整個(gè)系

表現(xiàn)為慢慢破壞和降解，而不是災(zāi)難性的破壞。

摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社??

要指出的是穩(wěn)健性(robustness)、穩(wěn)定性

(stability)或者是體內(nèi)恒

定理論(homeostasis)，概念相近，但又有不同。穩(wěn)健性是一個(gè)更

廣泛的概念，它主要是指維持系

功能的穩(wěn)定性；而穩(wěn)定性或者

體內(nèi)恒定規(guī)律是指維持系

狀態(tài)的穩(wěn)定性（即穩(wěn)定態(tài)）。一個(gè)穩(wěn)

健的系

可以有幾個(gè)不同的穩(wěn)定態(tài)，只要在不同的穩(wěn)定態(tài)下，該

系

都能維持它的功能，就稱為系

的穩(wěn)健性；一個(gè)系

可以在

不同穩(wěn)定態(tài)之

化，但仍維持了系

的功能，這也稱為系

的

穩(wěn)健性。比如一個(gè)細(xì)胞在極端的環(huán)境，如熱休克的狀態(tài)下，

常

會產(chǎn)生其他蛋白(如熱休克蛋白)來維持細(xì)胞的活性，使細(xì)胞進(jìn)入

另一個(gè)新的穩(wěn)定狀態(tài)，也稱為細(xì)胞的穩(wěn)健性。又如細(xì)菌在抗生素

作用下會產(chǎn)生抗

性，所以細(xì)菌就由不抗

狀態(tài)

成抗

狀態(tài)，

即細(xì)菌有系

的穩(wěn)健性，可以在抗生素條件下生存。再如艾滋病毒能以很高的突

率來應(yīng)付機(jī)體的免疫系

以及綜合療法，

即艾滋病毒可以根據(jù)DNA的突

產(chǎn)生無窮多的穩(wěn)定狀態(tài)來維持

其生命和致病性。摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社圖1.2

穩(wěn)健性(robustness)、穩(wěn)定性(stability)或者體內(nèi)恒定(homeostasis)。假定系

的起始狀態(tài)在穩(wěn)定態(tài)1的中心，一個(gè)系擾可以把系

推到穩(wěn)定態(tài)1的

緣，但系

仍可回到穩(wěn)定態(tài)1，

這就是系

的穩(wěn)定性和體內(nèi)恒定。如在擾后，系

轉(zhuǎn)折到穩(wěn)定態(tài)2，系

即喪失穩(wěn)定態(tài)1的穩(wěn)定性，并在穩(wěn)定態(tài)2狀態(tài)下達(dá)到新的穩(wěn)定性。如果系

在穩(wěn)定態(tài)2的功能與穩(wěn)定態(tài)1相比是不

的，則可以說系

具有穩(wěn)健性。在極端的情況下，系

可以在多種不同的穩(wěn)定態(tài)中轉(zhuǎn)

而保持其穩(wěn)健性。Complexity

in

interactionsA

complex

problem–?35,000

genes

either

on

or

off

(huge

simplification!)

would

have

2^35,000

solutions–?Things

can

be

simplified

by

grouping

andfinding

key

genes

which

regulate

manyother

genes

and

genes

which

may

onlyinteract

with

one

other

gene–?In

reality

there

are

lots

of

subtle

interactions

and

non-binary

states.Some

real

numbers

from

E.coli??

630

transcription

units

controlled

by

97

transcription

factors.??

100

enzymes

that

catalyse

more

than

one

biochemicalreaction

.??

68

cases

where

the

same

reaction

is

catalysed

by

more

thanone

enzyme.??

99

cases

where

one

reaction

participates

in

multiplepathways.??

The

regulatory

network

is

at

most

3

nodes

deep.??

50

of

85

studied

transcription

factors

do

not

regulate

othertranscription

factors,

lots

of

negative

auto-regulationTheoretical

hurdles

to

jump??

Switching

delay

(McAdams

and

Arkin

1997)–?

More

transcripts,

less

protein/transcript

=

more

energy

lessnoise–?

Fewer

transcripts,

More

protein/transcript

=

less

energymore

noise.–?

Selection

drives

this

trade-off–?

Two

critical

times;

how

long

after

trigger

does

a

protein

reach

a

critical

level

how

long

after

removal

of

the

trigger

does

the

protein

level

decline

to

below

critical

level.–?

How

critical

is

the

levelComplexity??

Simulations

found

3-20

minutes

from

transcript

toactive

protein.??

Many

processes

are

stochastic

(random)

notdeterministic.??

The

probabilities

are

definitely

skewed

but

still

havelong

tails–?

This

means

that

with

a

large

population

there

are

cells

which

may

be

in

very

different

states

than

most

of

the

rest

of

the

population.–?

Complex

interplay

between

regulation,

lag

and

activity

thathas

implications

when

trying

to

reconstruct

a

network.Networks-the

“system”

ofsystems

biology??

Humans

produce

some

pretty

complex

structures.–?

Computer

chips–?

Oil

refineries–?

Airplanes??

The

goals

for

these

structures

are

similar

to

life

forms–?

Survive–?

Do

it

at

a

cheap

cost–?

Reproduce/evolve??Basic

network

terminology??

Nodes??

Edges??

Scale-free–?

Power

laws–?

Exponential/Random

networks??

Robustness–?

Ability

to

respond

to

different

conditions–?

Robust

yet

fragile??

Complexity–?

Not

the

number

of

parts…

consider

a

lump

of

coal–?

The

number

of

different

parts

AND

the

organization

of

thoseparts摘自：

系

生物學(xué)，林標(biāo)揚(yáng)主編，浙江大學(xué)出版社Graph

theory,

networks??

Two

types

ofnetworks–?

Exponential

and

scalefree–?

Most

cellular

networksare

scale

free–?

It

makes

the

mostsense

to

study

theinteractions

of

thecentral

nodes

not

theouter

nodesHigh

Throughput

data

sources??

Microarray

data–?

Already

well

covered

in

the

last

couple

of

weeks.–?

Probably

the

most

mature??

Proteomics–?

Several

processes??

Separation

of

the

products??

Digest

the

products??

Find

the

mass

of

the

products–?

Problems??

Contamination??

Phosphorylation,

glycosylation,

Acylation,

methylation,cleavage.Cytoscape??

Software

tool

to

manage

data

and

develop

predictive

models(Genome

Research

Shannon

et

al.

2003)??

Not

directed

specifically

to

a

cellular

process

or

diseasepathway??

Combine–?

Protein-protein

interactions–?

RNA

expression–?

Genetic

interactions–?

Protein-dna

interactions–?

Protein

abundance–?

Protein

phosphorylation–?

Metabolite

concentrations??

Integrate

(global)

molecular

interactions

and

statemeasurements.??

Organized

around

a

network

graphSurviving

heat

shock:

Control

strategies

for

robustness

andperformance??

Taking

engineering

principles

and

applying

them

to

systems

biologyAir

conditioning??????????Set

point

(temperature

you

set)Sensor

(thermostat)Error

signal

(temp

exceeded)Controller

(thermostat/ac)Actuator

(ac

on)Heat

shock

protein??

Increased

heat

->

mRNA

-δ32mRNAmelting??

Make

δ32–?Interacts

with

RNAP

to

activate

specificsub-sets

of

genes??

Make

a

bunch

>10,000

protein

copies

todeal

with

heatHeat

shock

responseComponents??

DNAK–?

Chaperone

representative??

Binds

to

δ32and

degraded

proteins??

FtsH–?

Protease

degrading

δ32–?

Titrated

away

by

degraded

proteins??

δ32–?

Temperature

regulation

at

translationWhy

make

it

more

difficult???

Need

to

turn

off

(cooler)??

Don’t

want

to

activate

inappropriately

(energywaste)??

Fast

response

(proteins

degrading)??

Proportional

response

(it’s

a

little

hot)Theoretical

types

of

controlSummary??

Sometimes

simple

is

better

but:??

Often

some

complexity

adds

desirablefeatures??

Trade

off

between

complexity,robustness,

and

economy??

Modules,

reuse–?“Helps”

evolution–?Can

help

biologistTechniques

for

complexity??

Advanced

Methods

and

Algorithms

for

BiologicalNetworks

Analysis“such

questions

are

conventionally

viewed

as

computationally

intractable.

Thus,

biologists

and

engineers

alike

are

often

forced

to

resort

to

inefficient

simulation

methods

or

translate

their

problems

into

biologically

unnatural

terms

in

order

to

use

available

algorithms;

hence

the

necessity

for

an

algorithmic

scalable

infrastructure

the

systematically

addresses

these

questions”Problems

of

modeling??

Compare

model

to

data–?But

with

complex

model

and

largeparameter

set

any

data

set

can

be

made

tofit–?Could

a

simpler

model

also

work–?Untested

parametersAlternative

to

exhaustivesearches??

Use

sum

of

squares

to

generate

dynamicalbehavior

barriers–?

Don’t

test

all

possible

values

just

see

where

theymake

a

difference??

Stocastic

simulation

is

another

way

but–?

Uses

months

to

simulate

picoseconds??

Robustness

provides

a

key–?

Biological

systems

must

exhibit

robustness–?

This

robustness

also

limits

the

search

spaceA

Grand

ConvergenceNanotechnologySystems

biologyGenetics,

genomics

Technology

Has

TransformedContemporary

Systems

BiologyQuantitative

measurements

for

all

types

of

biologicalinformation.Global

measurements--measure

dynamic

changes

in

all

genes,mRNAs,

proteins,

etc,

across

state

changes.Computational

and

mathematically

integrate

different

data

types--DNA,

RNA,

Protein,

Interactions,

etc.--to

capture

distinct

types

of

environmental

information.Dynamic

measurements--across

developmental,

physiologicaldisease,

or

environmental

exposure

transitions.Utilization

of

carefully

formulated

systems

perturbations.Integration

of

discovery-

and

hypothesis-driven

(global

or

focused)

measurements

.

Perturbation--measurement--model--

hypothesis--perturbation--etc.Sixessentialfeaturesofcontemporarysystemsbiology

SystemsDynamic

Networks??????????Elements

(genes,proteins)

–

“nodes”Interactions

between

the

elements

–“edges”--dynamicElements

and

their

interactions

are

affectedby

the

Context

of

other

systems

within--cells

and

organismsInteractions

between/among

elements

giverise

to

the

system’s

Emergent

propertiesUnique

features

–?

Global–?–?Integrate

different

data

typesMillion

of

data

measurementsTwo

Types

of

DigitalInformation

Encode

TwoDifferent

Types

of

Networks??

Genes

encode

protein

networks

andprotein

machines??

Cis-control

elements,

together

with

their

cognate

transcription

factors,

specify

the

architecture

of

gene

regulatory

networksMostSophisticatedGeneRegulatory(andProtein)NetworkDefinedtoDateLevels

of

Biological

Information

DNA

mRNA

ProteinProtein

interactions

and

biomodulesProtein

and

gene

networks

Cells

Organs

IndividualsPopulationsEcologiesData

Integration,

Managementand

Modelingof

a

SystemNano

LabDetailed

GraphicRepresentation

CYTOSCAPE

Kinetic

model

of

Galactose

UtilizationNan

o

LabG4D_DNA4'G80D_G4D_DNA4'G4D_DNA80'G80D_G4D_DNA80'G4D_DNA3'G80D_G4D_DNA3'G3D_G80D'

=

kf*G4D_free*DNA4

-

kr*G4D_DNA4=

kf*G80D_free*G4D_DNA4

-

kr*G80D_G4D_DNA4

=

kf*G4D_free*DNA80

-

kr*G4D_DNA80=

kf*G80D_free*G4D_DNA80

-

kr*G80D_G4D_DNA80

=

kf*G4D_free*DNA3

-

kr*G4D_DNA3=

kf*G80D_free*G4D_DNA3

-

kr*G80D_G4D_DNA3

=

10*kf*G3D_free*G80D_free

-

kr*G3D_G80DG4_RNA'G80_RNA'G3_RNA'G4_proteinG80_protein=

0.1*kt*(G4D_DNA4+ubiq_in)*(1-G80D_G4D_DNA4)

-

0.1*kr*G4_RNA

=

kt*G4D_DNA80*(1-G80D_G4D_DNA80)

-

0.01*kr*G80_RNA=

0.1*kt*(galactose*G4D_DNA3)*(1-G80D_G4D_DNA3)

-

0.01*kr*G3_RNA

=

delay(G4_RNA,4)

=

delay(G80_RNA,4)G3_proteinG4D_totalG80D_totalG3D_total=

delay(G3_RNA,4)=

G4_protein/2=

G80_protein/2=

G3_protein/2G4D_freeG80D_freeG3D_freeDNA4DNA80DNA3kfkrktgalactoseubiq_in

=

G4D_total

–

(G4D_DNA4+G4D_DNA80+G4D_DNA3+G80D_G4D_DNA4+G80D_G4D_DNA80+G80D_G4D_DNA3)

=

G80D_total

-

(G3D_G80D+G80D_G4D_DNA4+G80D_G4D_DNA80+G80D_G4D_DNA3)

=

G3D_total

-

(G3D_G80D)

=

1

-

(G4D_DNA4

+

G80D_G4D_DNA4)=

1

-

(G4D_DNA80

+

G80D_G4D_DNA80)

=

1

-

(G4D_DNA3

+

G80D_G4D_DNA3)

=1

=1

=

10

=

STEP(10,

500)=

10MathematicalRepresentationof

a

System051015202530354045

Leading

Institutions

in

Systems

biologyInstitute

for

Systems

Biology(US)

MIT

(US)

Weizmann

Institute

(IL)

UCSD

Systems

Biology(US)

Caltech

(US)

Kitano

Inst.(JP)

Keio

University(JP)

Harvard

(US)

Free

UniversityAmsterdam

(NL)

Stanford

(US)

Number

of

top

3

votes

(N=137)Source:

EUSYSBIO

survey

by

Fraunhofer

ISI

2004Case

study-Galactoseutilization

in

yeast–?Classic

last