Yuki Juan’s Systems Biology Lab Systems Biology Hsueh-Fen Juan ( 阮雪芬 ) NTUT Aug 29, 2003.

Yuki Juan’s Systems Biology Lab

Systems BiologySystems Biology

Hsueh-Fen Juan (Hsueh-Fen Juan (阮雪芬阮雪芬 ))NTUTNTUTAug 29, 2003Aug 29, 2003

juan SBL

OutlineOutline

IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case study: effect of RGD-peptides in Case study: effect of RGD-peptides in

breast cancerbreast cancer

juan SBL

Traditional Biology & Traditional Biology & Systems BiologySystems Biology Traditional biologyTraditional biology : :

– Single genes or proteinsSingle genes or proteins

Systems biologySystems biology: : – Simultaneously study the complex Simultaneously study the complex

interaction of many levels of biological interaction of many levels of biological information to understand how they work information to understand how they work togethertogether

Genomic DNAGenomic DNA mRNA mRNA ProteinsProteins

Functional proteinsFunctional proteinsInformational pathways Informational pathways Informational networksInformational networks

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Systems Biology and OmicSystems Biology and Omics Datas Data

Systems BiologySystems Biology

GeneticGenetic

ProteomicProteomic

TranscriptomicTranscriptomic

MetabonomicMetabonomic

Drug discovery Drug discovery Development processDevelopment process

Understanding drug toxicologyUnderstanding drug toxicology

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The Aims of Systems The Aims of Systems BiologyBiology What are the basic structures and

properties of a biological network? How does a biological system behave

over time under various conditions? How does a biological system

maintain its robustness and stability? How can we modify or construct

biological systems to achieve desired properties?

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To Understand To Understand Biological SystemsBiological Systems System structure identificationSystem structure identification System behavior analysisSystem behavior analysis System controlSystem control System designSystem design

juan SBL

OutlineOutline

IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case biology study: effect of RGD-peCase biology study: effect of RGD-pe

ptides in breast cancerptides in breast cancer

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System Structure System Structure IdentificationIdentification Network structure identificationNetwork structure identification

– KEGG and EcoCycKEGG and EcoCyc Parameter identificationParameter identification

– Genetic algorithmsGenetic algorithms

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KEGGKEGG

http://www.genome.ad.jp/kegg/

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Pathway in KEGGPathway in KEGG

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EcoCycEcoCyc

http://www.ecocyc.org/http://www.ecocyc.org/

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Pathway in EcoCycPathway in EcoCyc

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Genetic AlgorithmsGenetic Algorithms

Based on the underlying genetic process

They are replicated and passed onto the next generation with selection depending on fitness.

Genetic information can be changed through genetic operations.

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Three Main Operations Three Main Operations in GAin GA Selection Crossover Mutation

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Genetic AlgorithmsGenetic Algorithms

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System Behavior System Behavior AnalysisAnalysis SimulationSimulation Analysis methodsAnalysis methods

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Software Tools for Software Tools for Systems Biology and Systems Biology and Their WorkflowTheir Workflow Relationship among software Relationship among software

toolstools Workflow and software toolsWorkflow and software tools

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Relationship among Relationship among Software ToolsSoftware Tools

Experimental data Experimental data databasedatabase

Experimental data Experimental data interfaceinterface

Measurement Measurement systemssystems

Genome/proteomeGenome/proteomedatabasedatabase

System structure System structure databasedatabase

SimulatorSimulator

System analysisSystem analysismodulemodule

System profileSystem profiledatabasedatabase

Visualization moduleVisualization module

Parameter optimiztionParameter optimiztionmodulemodule

Hypotheses generation Hypotheses generation experiment planning experiment planning

modulemodule

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Workflow and Workflow and Software ToolsSoftware Tools

Expression profile dataTwo-hybrid dataRT-PCR data, etc.

Gene regulation networkMetabolic cascade networkSignal transduction network

Parameter optimizer

Simulator

Hypothesisgenerator

Biological experiments

A set of plausible hypothesisPredictions of gene and interactions

Experiment designAssistance system

Experiment plans

Dynamic system analysisRobustness stability, bifurcation, etcDesign pattern analysisDesign patter extraction

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Robustness of Robustness of Biological SystemBiological System System controlSystem control RedundancyRedundancy Modular designModular design Structural stabilityStructural stability

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System ControlSystem Control

Feedforward ControlFeedforward Control Feedback ControlFeedback Control

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Feedforward Control and Feedforward Control and Feedback ControlFeedback Control

ControlleControllerr

EffectorEffectorinputinput outputoutput

Feedforward controlFeedforward control

ControlleControllerr

EffectorEffectorinputinput outputoutput

Feedback controlFeedback control

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Heat Shock Response with FeHeat Shock Response with Feedforward and Feedback Conedforward and Feedback Controltrol

Heat ShockHeat Shock

Misfolded Misfolded ProteinProtein

Normal Normal ProteinProtein rpoHrpoH

EE

7070

3232

3232

3232dnaKdnaKdnaJdnaJgrpEgrpE

dnaKdnaKdnaJdnaJgrpEgrpEGroESGroESGroELGroEL

hsphspdnaKdnaKdnaJdnaJGroESGroESGroELGroEL

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Redundancy in MAP kinase Redundancy in MAP kinase cascadecascade

MEKK1, MLK3Raf, Mos

MEK1,2/MKK1,2

MAPK/ERK

SEK1, 2/MKK4,7

SAPK/JNK

ASK1, TAK1

MKK3,6

p38

TranscriptionTranscription

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Modular DesignModular Design

Component: Component: – An elemnetary unit of the systemAn elemnetary unit of the system– Genes and proteinsGenes and proteins

Device:Device:– An minimum unit of the functional assemblyAn minimum unit of the functional assembly– Transcription complexes and replication complexesTranscription complexes and replication complexes

Module:Module:– A large cluster of devicesA large cluster of devices– Organells and gene regulatory circuits for the cell cycleOrganells and gene regulatory circuits for the cell cycle

SystemSystem– A top-level assembly of modulesA top-level assembly of modules– A cell or entire animalA cell or entire animal

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Structural StabilityStructural Stability

Play important roles in developmentPlay important roles in development Temporal arrangement of signaling iTemporal arrangement of signaling i

nn– the JAK/STAS signaling pathwaythe JAK/STAS signaling pathway– pattern formation in pattern formation in DrosophilaDrosophila involvi involvi

ng Ubx and Dpp ng Ubx and Dpp

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Genome, Proteome, and SGenome, Proteome, and Systeomeysteome

Dynamics informationDynamics information

High resolutionimage

Expression profileProtein interactions, etc.

Proteome

Genome

Components Components informationinformation

Basic model informationBasic model information

Parameters

Gene network modelMetabolic pathway modelSignal transduction model

Basic structure

Individual genetic variationsIndividual genetic variations

System dynamicsanalysis

Individual sequence variation

Individual heterochromatinvariation

Mutation analysis

Drug sensitivityanalysis

System System dynamics dynamics informationinformation

Individual Individual SysteomeSysteome

juan SBL

OutlineOutline

IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery The systems biology study: sffect of The systems biology study: sffect of

RGD-peptides in breast cancerRGD-peptides in breast cancer

juan SBL

Introduction to Protein—pIntroduction to Protein—protein Interactionrotein Interaction Protein-protein interactions are Protein-protein interactions are

intrinsic to every cellular process.intrinsic to every cellular process. Form the basis of phenomenaForm the basis of phenomena

-DNA replication and transcription-DNA replication and transcription

-Metabolism -Metabolism

-Signal transduction-Signal transduction

-Cell cycle control -Cell cycle control

-Secretion-Secretion

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PPIPPI

Knowledge of interacting proteins

Provide insight into the function of important genes

Elucidates relevant pathways

Facilitates the identification of potential drug targets

Use in developing novel therapeutics

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The Study of Protein-The Study of Protein-protein Interaction by protein Interaction by Mass SpectrometryMass Spectrometry

bait

S14

??

??

* *

**

SDS-PAGE

MASS

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Peptide Mass Peptide Mass FingerprintingFingerprinting

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Yeast Two-hybrid Yeast Two-hybrid SystemSystem

Useful in the study of various interactionsUseful in the study of various interactions The technology was originally developed The technology was originally developed

during the late during the late 1980's1980's in the laboratory Dr. in the laboratory Dr. Stanley Fields (see Fields and Song, 1989, Stanley Fields (see Fields and Song, 1989, NatureNature). ).

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Yeast Two-hybrid Yeast Two-hybrid AssayAssay

GAL4 DNA-binding

domain

GAL4 DNA-activation domain

Nature, 2000

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Yeast Two-hybrid Yeast Two-hybrid AssayAssay Library-based Library-based

yeast two-yeast two-hybrid hybrid screening screening methodmethod

Nature, 2000

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Protein-protein Protein-protein Interactions on the Interactions on the WebWeb

Yeast Yeast http://depts.washington.edu/sfields/yplm/data/indexhttp://depts.washington.edu/sfields/yplm/data/index.htm.htmll http://portal.curagen.comhttp://portal.curagen.com http://mips.gsf.de/proj/yeast/CYGD/interaction/http://mips.gsf.de/proj/yeast/CYGD/interaction/ http://www.pnas.org/cgi/content/full/97/3/1143/DC1http://www.pnas.org/cgi/content/full/97/3/1143/DC1 http://dip.doe-mbi.ucla.edu/http://dip.doe-mbi.ucla.edu/ http://genome.c.kanazawa-u.ac.jp/Y2Hhttp://genome.c.kanazawa-u.ac.jp/Y2H C. ElegansC. Elegans http://cancerbiology.dfci.harvard.edu/cancerbiology/ResLabs/Vidal/http://cancerbiology.dfci.harvard.edu/cancerbiology/ResLabs/Vidal/ H. PyloriH. Pylori http://pim/hybrigenics.comhttp://pim/hybrigenics.com DrosophilaDrosophila http://gifts.univ-mrs.fr/FlyNets/Flynets_home_page.htmlhttp://gifts.univ-mrs.fr/FlyNets/Flynets_home_page.html

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Yeast Protein Linkage Yeast Protein Linkage Map DataMap Data

New protein-protein interactions in yeastNew protein-protein interactions in yeast

Stanley Fields Lab

http://depts.washington.edu/sfields/yplm/data

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GeneScapeGeneScape

PathwayCalling: Protein interaction PathwayCalling: Protein interaction and pathway Analysisand pathway Analysis

http://portal.curagen.com

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Munich Information Munich Information Center for Protein Center for Protein SequencesSequences

MIPS: a database for genomes and protein MIPS: a database for genomes and protein sequencessequences

The MIPS Comprehensive Yeast Genome The MIPS Comprehensive Yeast Genome Database (CYGD) aims to present information Database (CYGD) aims to present information on the molecular structure and functional on the molecular structure and functional network.network.

http://mips.gsf.de/

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Yeast Interacting Yeast Interacting Proteins DatabaseProteins Database

http://genome.c.kanazawa-u.ac.jp/Y2H

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SuisekiSuiseki

•DNA replication •The Immune System •The E2F transcription factor •The talin/viniculin/actin system

is a system for the extraction of protein-protein interactions from large collections of scientific text

http://www.pdg.cnb.uam.es/suiseki/

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Suiseki Suiseki

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SuisekiSuiseki

RegulateRegulate ActivateActivate

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Information Extraction Information Extraction (IE)(IE) A vast amount of data on protein-A vast amount of data on protein-

protein interactions residues in protein interactions residues in the published literature, which the published literature, which never been entered into never been entered into databases.databases.

IE have been applied to gaining IE have been applied to gaining information on protein-protein information on protein-protein interactions.interactions.

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Mining Literature for Mining Literature for Protein-protein Protein-protein InteractionsInteractions

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Extraction of the Extraction of the InteractionsInteractions The nouns and verbs are taken from The nouns and verbs are taken from

a hand constructed list containing na hand constructed list containing nouns such as ouns such as activationactivation, , phosphorylphosphorylationation or or interactioninteraction, and verbs such , and verbs such as as activatesactivates, , bindsbinds oror phosphorylatephosphorylatess. Rules are applied directly to the te. Rules are applied directly to the text by string comparison.xt by string comparison.

Comp Funct Genom 2001, 2, 196-206

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Extraction of the Extraction of the InteractionsInteractions The sentence “The expressed The sentence “The expressed p53p53 p p

rotein showed nuclear localization arotein showed nuclear localization and its expression was associated witnd its expression was associated with an induction of h an induction of p21p21 and and baxbax expres expression” relates sion” relates p53p53 with with p21p21 and and baxbax but does not imply a physical interacbut does not imply a physical interaction between them.tion between them.

Comp Funct Genom 2001, 2, 196-206

juan SBL

OutlineOutline

IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery The systems biology study: sffect of The systems biology study: sffect of

RGD-peptides in breast cancerRGD-peptides in breast cancer

juan SBL

Linkage of a Basic System-Linkage of a Basic System-Biology Research Cycle with Biology Research Cycle with Drug Discovery and Drug Discovery and Treatment CyclesTreatment Cycles

Nature 2002, 420, 206..

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Mammalian System-microbiMammalian System-microbial-nutritional-xenobiotic Intal-nutritional-xenobiotic Interactionseractions

Nature 2003, 2, 668.

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Possible InteractionsPossible Interactions

Nature 2003, 2, 668.

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The Dynamic Pachinko The Dynamic Pachinko Model of MetabolismModel of Metabolism

Nature 2003, 2, 668.

juan SBL

OutlineOutline

IntroductionIntroduction To understand biological systemsTo understand biological systems Protein—protein interactionProtein—protein interaction Drug DiscoveryDrug Discovery Case study: effect of RGD-peptides Case study: effect of RGD-peptides

in breast cancerin breast cancer

juan SBL

Effect of RGD-peptides Effect of RGD-peptides in breast cancerin breast cancer

IntroductionIntroduction cDNA microarray cDNA microarray ProteomicsProteomics BioinformaticsBioinformatics


IntroductionIntroduction

juan SBLSCIENCE, 2001, 294, 82-85

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The Structure of an IntegriThe Structure of an Integrinn HynesHynes in 1987 to emin 1987 to em

phasize the role of thphasize the role of these RGD receptors in ese RGD receptors in integratingintegrating the extra the extracellular matrix outsicellular matrix outside the cell with the ade the cell with the actin-containing cytosctin-containing cytoskeleton inside the cekeleton inside the cell.ll.

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The Interactions of Integrins The Interactions of Integrins with Other Proteins on both Siwith Other Proteins on both Sides of the Lipid Bilayerdes of the Lipid Bilayer

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Schematic Model of the ProteiSchematic Model of the Protein-protien Interactions of a Focan-protien Interactions of a Focal Adhesion Complexl Adhesion Complex

Signal are presumably Signal are presumably transmitted into the transmitted into the nucleus, where they nucleus, where they stimulate the stimulate the transcription of gene transcription of gene involved in involved in cell growthcell growth and and proliferationproliferation

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Integrin-activated Survival SiIntegrin-activated Survival Signalsgnals

Integrins

Shc

FAK

Grb2/Sos

PI 3-kinase

Ras Raf MEK MAPK

Cell survival

Trends in Cell Biology, 1997, 7, 146-150

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How RGD Trigger How RGD Trigger Apoptosis?Apoptosis?

By integrin-mediated signal? By integrin-mediated signal? Directly interact with the protein in cDirectly interact with the protein in c

ytosol?ytosol?

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How RGD Trigger How RGD Trigger Apoptosis?Apoptosis?

b. RGD trigger apoptosis via integrin

Nature, 1999, 397, 534-539

a. Cell survive

c. Cell apoptosis by activating procaspase-3

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Control Aggregation Cell Death

RGD(Arg-Gly-Asp) is the smallest motif that bind with the integrin receptor on the cell surface and play important role in cell cycle.

RGD and Cell DeathRGD and Cell Death


Our StudyOur Study

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Human breast cancer cell MCF-7

Cell Apoptosis

Genomic Study

Proteomics

Bioinformatics

Our Study

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The Structures of RGD The Structures of RGD Mimetic PeptidesMimetic Peptides

Asp

GlyArg

NH

H2N O

O

N

O

HN

NH

O

O

OH

HN

O

HN

O

S

S

HN

O

NH

NH

H2N

ArgGly Asp

Trp

Pro

Cys

Tpa

Cyclic-RGD

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RGD cRGDcontrol

1mM

5mM

0.5mM

1mM

control


cDNA MicroarraycDNA Microarray

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Introduction to MicroarrayIntroduction to Microarray After the draft of Human genome project was puAfter the draft of Human genome project was pu

blished and the powerful high–throughput microblished and the powerful high–throughput microarray technology is available, the discovery of disarray technology is available, the discovery of discriminating gene patterns becomes important.criminating gene patterns becomes important.

cDNA microarray technology is a powerful approcDNA microarray technology is a powerful approach to accurately measure changes in global mRach to accurately measure changes in global mRNA expression levels.NA expression levels.

This technique has been used to discover novel gThis technique has been used to discover novel genes, determine gene functions, evaluate drugs, enes, determine gene functions, evaluate drugs, dissect pathways, and classify clinical samples.dissect pathways, and classify clinical samples.

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A Framework of Microarray Analysis

Experiments Designing

Microarrary Analysis

Image Analysis

Data Analysis

Data Preprocessing(Normalization & Data Filtering )

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cDNA MicroarraycDNA Microarray

C-RGD, 6hr C-RGD, 24hr

C-RGD, 48hr C-RGD, 72hr

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Apoptosis Apoptosis

Total Total 3434 genes, but after genes, but after filtering there are only filtering there are only 1919 genesgenes

Total Total 1111 genes have genes have expression fold >2 (up or expression fold >2 (up or down changes)down changes)

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Apoptosis RegulatorApoptosis Regulator

U60519

U97075

AF051941

U13738

AF005775

U60521

Z48810

AAF19819

U67319

U28976

AF015450

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DescriptionGenebankaccession

No.

6 hFold Change

24 hFold Change

48 hFold Change

72 hFold Change

Group 1

caspase 10, apoptosis-related cysteine protease U60519 - - - 0.471

CASP8 and FADD-like apoptosis regulator U97075 - - - 0.355

nucleoside diphosphate kinase type 6 (inhibitorof p53-induced apoptosis-alpha) AF051941 - - - 0.376

Group 2

caspase 3, apoptosis-related cysteine protease U13738 - 2.301 - -

CASP8 and FADD-like apoptosis regulator AF005775 - 2.272 - -

Group 3

caspase 9, apoptosis-related cysteine protease U60521 - - 2.519 -

Group 4

caspase 4, apoptosis-related cysteine protease Z48810 2.615 - 2.796 2.819

Group 5

inhibitor of apoptosis protein AAF19819 - - - 5.249



Group 6

CASP8 and FADD-like apoptosis regulator AF015450 - - - 6.912

Apoptosis RegulatorApoptosis Regulator

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6 7224 48

time (hour)0.01

0.1

1

10

Normalized Intensity(log scale)

p1

6 7224 48

time (hour)0.01

0.1

1

10


p1

6 7224 48

time (hour)0.01

0.1

1

10


p1

6 7224 48

time (hour)0.01

0.1

1

10


p1

6 7224 48

time (hour)0.01

0.1

1

10


p1

6 7224 48

time (hour)0.01

0.1

1

10


p1

juan SBL

Using Linear Model to Using Linear Model to Construct Gene NetworkConstruct Gene Network

Linear ModelLinear Model

ti;,btywΔt

tΔy

jiji,j

i

tBtyWt

ty

,

1~~~

YYYt

YW TT

D’haeseleer, 2000

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Weights Matrix of Weights Matrix of Apoptosis RegulatorApoptosis Regulator

Weights Gene 1 Gene 2

2.670363 AF015450 U60521

2.068236 AAF19819 U60521

-1.889373 AF015450 AAF19819

-1.427408 AAF19819 AAF19819

-0.81632 AF005775 AF005775

-0.761848 U13738 AF005775

0.753277 AF015450 U60519

0.682257 U13738 AAF19819

0.646907 Z48810 AF005775

0.636552 AF015450 AF005775

0.632796 AF005775 Z48810

0.594627 AF005775 AAF19819

-0.55848 Z48810 AAF19819

0.543142 AAF19819 U60519

-0.527872 U60521 U60521

0.518056 U28976 U60521

0.508007 U60521 AF005775

0.499483 U13738 Z48810

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Gene Network of Gene Network of Apoptosis RegulatorApoptosis Regulator caspase 9, apoptosis-related

cysteine protease (U60521)

inhibitor of apoptosis protein (AAF19819)

CASP8 and FADD-like apoptosis regulator

(AF015450)

+2.068236

+2.670363

6

-1.889373

caspase 3, apoptosis-related cysteine protease

(U13738)

+0.682257

-0.761848

CASP8 and FADD-like apoptosis regulator

(AF005775)


(U60519)


(Z48810)

+0.646907

+0.636552

+0.753277

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J03071 X15215 S75361 M37483 U14187 D12614 NM_005130 AF179274 AB017365 AF035835 D25328 NM_003242 S81439 AB017364 L13858 L27475 AF068868 M34480 NM_005928 AF005271 X53038 AI127370 AF002986

M37763 AF107885 AF081513 AJ000185 AF251118

D10202

U12535 AF026692

X14253

X51602 AF119815 U72338 AF041240 M37435

AB000509

AI634668 S77035 AF107885 U52112 AF056087

AF019634 X76079

BE336944 X52599 L24494 M64347 M12783 AF107885

U52112 M83575 L13857 AI692949 AW663903 AJ222700 M57399 U31176 X03438 AI885899 AB009249 U73737 U66406 AF266504 AW887370 AB039723 M77227 AF010312 M35878 NM_005429 AAC17439 U28054 L13858 L34641

X14253

NM_004791 AF035835 D63395 U94888 J03071 D87845 L13857 M21188 NM_004114 X14885 X70340

1 2

Signal TransducerSignal Transducer

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Weights Gene1 Gene2

-0.875598 NM_003242 L274750.834468 M37435 L27476-0.78655 L27475 L27477

-0.760695 M21188 L27478-0.728793 D63395 L274790.645775 NM_003242 NM_005429

0.608239 NM_003242 NM_005430

0.558504 L27475 NM_005431

0.538674 D63395 NM_005432

0.530764 U72338 L274750.455149 AF041240 L274760.453129 AI634668 L27477-0.452645 AB017364 L274780.449854 NM_003242 U12535

0.444301 M21188 AF251118

-0.442259 M37435 U52112

0.437687 M21188 U12535

0.429097 NM_003242 AF251118

Weights Matrix of Weights Matrix of Signal TransducerSignal Transducer

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transforming growth factor,

beta receptor II (70-80kD)

(NM_003242) Human interleukin-1 beta

converting enzyme gene, 5' flank.

(L27475)

colony stimulating

factor 1 (macrophage)

( M37435)

insulin-degrading enzyme

(M21188)

Notch (Drosophila)

homolog 4

(D63395)

-0.875598

+0.834468

vascular endothelial growth factor C

(NM_005429)

+0.645775

+0.608239

+0.558504

Human platelet activating factor

acetylhydrolase, brain isoform, 45

kDa subunit (LIS1) gene, exon 7.

(U72338)

hypocretin (orexin)

neuropeptide precursor

(AF041240)

+0.455149

msh (Drosophila) homeo box

homolog 1 (formerly homeo box 7)

(AI634668)

+0.453129 frizzled (Drosophila) homolog 2

(AB017364)

epidermal growth factor

receptor pathway substrate 8

(U12535)

+0.449854

Interleukin-1 Superfamily z

(AF251118)

+0.444301

+0.530764

-0.452645

-0.728793

+0.538674

-0.760695

Gene Network of Gene Network of Signal TransducerSignal Transducer


ProteomicsProteomics

juan SBLNature 2000, 405, 837-846

Two-dimensional Gel Approach

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Control cRGD

97000

66000

45000

30000

14400

20100

4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5 4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5

1

Control vs c-RGD (6hr)Control vs c-RGD (6hr)

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97000

66000

45000

30000

14400

20100

4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5

2

34

5 6 78

910

11 13

14

121516

17

18

Control c-RGD


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97000

66000

45000

30000

14400

20100

4.0 5.0 6.0 7.0 10.08.0 9.05.5

19 20

21 22


Control c-RGD

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97000

66000

45000

30000

14400

20100

4.0 5.0 6.0 7.0 10.08.0 9.03.2 5.5

23

24 25

26


Control c-RGD

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Proteomics ResultsProteomics Results

1. Semenogelin I protein precursor (S1. Semenogelin I protein precursor (SGI)GI)

2. Cell division protein kinase 6 2. Cell division protein kinase 6 3. 3. Zinc finger protein 74 isoform 4Zinc finger protein 74 isoform 4 4. K4. Keratin eratin 5. Similar to presomitic mesoderm sp5. Similar to presomitic mesoderm sp

ecific geneecific gene 6. Unnamed protein product6. Unnamed protein product 7. RNA-binding protein regulatory su7. RNA-binding protein regulatory su

bunit bunit 8. Similar to Claudin-6 (Skullin)8. Similar to Claudin-6 (Skullin) 9. Unnamed protein product 9. Unnamed protein product 10. Similar to Per-hexamer repeat pro10. Similar to Per-hexamer repeat pro

tein 5tein 5 11. Similar to L1 repetitive element O11. Similar to L1 repetitive element O

RFRF 12. Hypothetical protein12. Hypothetical protein 13. 13. Zinc finger protein 189 Zinc finger protein 189

ISOFORM 2ISOFORM 2

14. Hypothetical protein 14. Hypothetical protein 15. Similar to stretch response prote15. Similar to stretch response prote

in 553in 553 16. Zinc finger protein 8316. Zinc finger protein 83 17. Immunoglobulin heavy chain var17. Immunoglobulin heavy chain var

iable regioniable region 18. Hypothetical protein18. Hypothetical protein 19. Cytokeratin 819. Cytokeratin 8 20. Cytokeratin 820. Cytokeratin 8 21. Zinc-alpha-2-glycoprotein precu21. Zinc-alpha-2-glycoprotein precu

rsorrsor 22. Keratin 1822. Keratin 18 23. Platelet-activating factor acetylh23. Platelet-activating factor acetylh

ydrolase precursor ydrolase precursor 24. Topoisomerase II alpha24. Topoisomerase II alpha 25. 13kD differentiation-associated 25. 13kD differentiation-associated

proteinprotein 26. Purified protein derivative-specif26. Purified protein derivative-specif

ic T-cell receptor beta chainic T-cell receptor beta chain Red color: up-regulatedWhite color: down-regulated


BioinformaticsBioinformatics

juan SBL

RGD Peptides Can Be RGD Peptides Can Be Used in Many DiseasesUsed in Many Diseases

ThrombosisThrombosis OsteoporosisOsteoporosis CancerCancer

Any one elseAny one else

????

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Blood Clots FormBlood Clots Form

Blood clots form whBlood clots form when platelets adhere en platelets adhere to one another throto one another through fibrinogen bridgugh fibrinogen bridges that bind to the pes that bind to the platelet integrinlatelet integrin

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Clustering Analysis of Clustering Analysis of ProteinsProteins

http://uranus.csie.ntu.edu.tw:9000/index.jsp

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RGD-containing RGD-containing Proteins in Swiss-Prot Proteins in Swiss-Prot DatabaseDatabase In Swiss-Prot database, there areIn Swiss-Prot database, there are 738738

human RGD-containing proteins whichuman RGD-containing proteins which containing h containing 5 5 caspase proteins .caspase proteins .– Caspase 1, caspase 2, caspase 3 and caspCaspase 1, caspase 2, caspase 3 and casp

ase7, caspase 8.ase7, caspase 8.

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SM22 Leiomyoma

RGD-containing Proteins RGD-containing Proteins in Swiss-Prot Databasein Swiss-Prot Database

Heat shock protein DnaHeat shock protein DnaChaperone DnaJChaperone DnaJ Alzheimer's diseaseAlzheimer's disease


Conclusion and Conclusion and DiscussionDiscussion

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Conclusion and Conclusion and Discussion Discussion

Cyclic RGD exerts more potency than that of liner RGCyclic RGD exerts more potency than that of liner RGD on the inhibiting cell growth.D on the inhibiting cell growth.

The cyclic RGD exerts The cyclic RGD exerts 8-10 times8-10 times potency more than t potency more than that of liner RGD peptide in inhibiting proliferation anhat of liner RGD peptide in inhibiting proliferation and inducing clustering of MCF-7 cells.d inducing clustering of MCF-7 cells.

Cyclic RGD can induce the apoptosis of MCF7. We shoCyclic RGD can induce the apoptosis of MCF7. We showed many caspases involved in this apoptosis and cowed many caspases involved in this apoptosis and constructed the caspase pathway.nstructed the caspase pathway.

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Conclusion and Conclusion and DiscussionDiscussion

CASP8 and FADD-like apoptosis regulatorCASP8 and FADD-like apoptosis regulator, , caspase 9caspase 9 and and inhibitor of apoptosis proteininhibitor of apoptosis protein formed the positive and n formed the positive and negative feedback control system.egative feedback control system.

Vascular endothelial growth factor CVascular endothelial growth factor C and and human interlehuman interleukin-1 beta converting enzyme geneukin-1 beta converting enzyme gene have the important have the important positions in the gene network because they will affected positions in the gene network because they will affected many other genes.many other genes.

Clustering tool maybe could predict some novel functioClustering tool maybe could predict some novel functions in RGD-containing proteins.ns in RGD-containing proteins.

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OutlookOutlook

cDNA microarray

Drug discovery

Proteomics

Apoptosis pathway

Cellular mechanism

Bioinformatics

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SummarySummary

Systems biology is a new and Systems biology is a new and emerging field in biology.emerging field in biology.

Systems biology requires a range Systems biology requires a range of new analysis techniques, of new analysis techniques, measurement technologies, measurement technologies, experimental methods, software experimental methods, software tools.tools.

Systems Biology will be the Systems Biology will be the dominant paradigm in biology.dominant paradigm in biology.

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juan SBL

Thank you!Thank you!

Yuki Juan’s Systems Biology Lab Systems Biology Hsueh-Fen Juan ( 阮雪芬 ) NTUT Aug 29, 2003.

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Transcript of Yuki Juan’s Systems Biology Lab Systems Biology Hsueh-Fen Juan ( 阮雪芬 ) NTUT Aug 29, 2003.