• Reconstructing gene networks• Analysing the properties of gene networks

Gene NetworksUsing gene expression data to reconstruct gene networks

1)Parts list – genes, transcription factors, promoters, binding sites, …

2)Architecture – a graph depicting the connections of the parts

3)Logics – how combinations of regulatory signals interact (e.g., promoter logics)

4)Dynamics – how does it all work in real time

Gene Networksfour different levels

1)Parts list – genes, transcription factors, promoters, binding sites, …

2)Architecture – a graph depicting the connections of the parts

3)Logics – how combinations of regulatory signals interact (e.g., promoter logics)

4)Dynamics – how does it all work in real time

Gene Networksfour different levels

A Dw

Gene A Gene DrelationArc/EdgeNode Node

Networks and Graphsdescribing gene networks using graphs

G1 G2• The product of gene G1 is a transcription factor,

which binds to the promoter of gene G2 – physical networks

G1 G2

G1 G2

• Gene G2 is mentioned in a paper about gene G1 – literature networks

• The disruption of gene G1 changes the expression level of gene G2 – expression networks

Networks and GraphsDifferent interpretations of arcs

The dataset used is coming from Hughes et al.: “Functional discovery via a compendium of expression profiles”, Cell 102, 109-126 (2000)

• Yeast data, 6316 gene expression profiles over 300 experiments• 276 deletion mutants (274 single, 2 double)• 11 tet-promoter mutants• 13 compound treatments

selected a subset of 248 experiments:• Single deletion mutants• All chromosomes present

Gene Disruption Networksthe dataset

The normalized expression log(ratios) are discretized using the threshold :

Discretization of the dataHughes et al.

X < d(X) = 1 X d(X) = 0X > d(X) = 1

A B Cgene B

gene C

gene D

gene AA D

B C

Network constructiondisruption network

outdegree = 4indegree = 3

Indegree and Outdegreedegree of a node

Indegree

Most genes have only a few incoming / outgoing edges, but some have many (>500)

Outdegree

Indegree and Outdegreedegree distributions

Indegree and Outdegreepowerlaw distribution

outdegree m n indegree m n2.0 Carbohydrate metabolism 363 4 Amino-acid metabolism 9 194

RNA turnover 353 4 Nucleotide metabolism 6 82Meiosis 244 3 Energy generation 5 242Cellstress 207 9 Small molecule transport 5 343Protein translocation 197 3 Other metabolism 5 148

2.8 RNA turnover 110 4 Amino-acid metabolism 4 167Cellstress 62 8 Nucleotide metabolism 3 67Meiosis 54 3 Energy generation 2 184Proteinsynthesis 53 7 Differentiation 2 43Cellwallmaintenance 47 6 Small molecule transport 2 286

3.6 RNA turnover 48 4 Small molecule transport 2 230RNA processing/ modification 41 4 Other metabolism 2 96Cellstress 27 8 Nucleotide metabolism 2 58Small molecule transport 19 8 Matingresponse 2 57Cellwallmaintenance 19 6 Amino-acid metabolism 2 133

Cellular role table showing the top 5 groups with the highest median degrees for the networks with a minimum group size of 3 for outdegree and 40 for the indegree (: significance threshold, m: the median degree, n: the group size)

Median Out-/Indegree

• Is there one “big” dominant connected component and possibly a number of small components, or several components of comparable sizes?

• Can the network be broken down in several components of comparable size by removing nodes of high degree (i.e., nodes with many incoming or outgoing edges)?

network modularity

network modularityNumber of connected components in the networks

network modularityNumber of connected components in the networks

component

full network

1% removed

5% removed

10% removed

2.0 largestsecond

total

5383

1

4707

1

368222

261452

3.0 largestsecond

total

355622

246122

138549

7646

17

4.0 largestsecond

total

235434

120537

5426

22

452851

Number of connected components in the networks

network modularity

• Wagner, Genome Research 2002 – there exist many independent modules

• Featherstone and Broadie, Bioessays 2002 - there is only one giant module

• All depends on the definition of the ‘module’

Modularity other opinions

a closer look

AEP2

AKR1

CMK2

ANP1

RAD16

AFR1

CEM1

CUP5

SST2

DIG1

UBP10

STE2

ERG2

PHO89ERG6

GAS1 PTP2

GYP1

HIR2HPT1

ISW1

FIG1 ISW2

KIN3

MAC1MRPL33

MSU1

NPR2

PET111

RAD57

RIP1

RRP6

ASG7

STE6RTS1

SCS7

SGS1

MFA1

SHE4AGA1

SWI4

FUS1SWI5

VAC8

VMA8

YAL004W

YAR014C

YEL044W

YER050C

FUS3

GPA1

BAR1

MFA2

YER083C

RTT104

YMR014W

YMR029C AGA2YMR031W-A

YMR293C

YOR078W

ADE2

AFG3

BNI1

CLA4

ERG3

FKS1

KAR4

YAR064W

CHS3

VAP1

ICS2

YCLX09W

YDL009C

STP4

PMT1

VCX1HO

THI13

ADR1

YDR249C PAM1

YDR275W

HXT7

HXT6 YDR366CYDR534C

URA3

YEL071W

MNN1

ICL1

RNR1

YER130C

YER135C

SPI1 DMC1

HSP12

NIL1

GSC2

KSS1

MUP1

YGR138C

SKN1

YGR250C

YHR097C YHR116W

YHR122W

YHR145C

YIL060W

YIL096C

YIL117C

RHO3

YIL122W FKH1

NCA3

YJL145W

RPL17B

YJL217W

CYC1

DAN1

PGU1

GFA1

HAP4

RRN3

STE3

PRY2

KTR2

SRL3

YLR040C

YLR042C

SSP120

HSP60

YLR297W

RPS22B YLR413W

HOF1

DDR48

RNA1

YMR266W

YNL078W

SPC98

YNL133C

YNL217W

WSC2YPT11

RFA2

YNR009W

YNR067C

MDH2

YOL154W

NDJ1

WSC3

CDC21

PFY1

RGA1

MSB1

SRL1

YOR248W

YOR296W

YOR338W

GDS1PDE2

FRE5

YPL080C

RPS9A

BBP1

YPL256C

SUA7

MEP3

YPR156C

HMG1

HOG1

MED2

QCR2

RAD6

RAS2

RPD3

RPS24A

CRS4CYC8

YAR031W

YBR012C

HIS7

YCLX07W

YCRX18C PCL2

YDR124W

ECM18APA2

YER024W

HOM3

THI5

YGL053W

NRC465

YGR161C YHR055C

YIL037C

YIL080W

YIL082W

HIS5

YJL037W

SAG1

CPA2

AAD10

HYM1

MET1

MID2

YML047C

KAR5

CIK1

FUS2 SCW10

BOP3

YNL279WTHI12

YOL119C

YOR203W

TEA1

ISU1

YPL156C

YPL192CYPL250C

KAR3YIL082W

-A

YML048W-A

YMR085W

STE11

STE12 STE18

URA1

URA4

STE24

STE4

STE5

STE7SWI6

MAK1

TUP1

YER044C YJL107C

This subnetwork is the result of filtering the full network at =2.0 for the core set marked in red and their next neighbours (red arcs: down- regulation, green arcs: upregulation).

Mating subnetwork

This subnetwork is the result of filtering the full network at =4.0 for the core set marked in red and their next neighbours (red arcs: downregulation, green arcs: upregulation).

Mating subnetwork

AKR1

PHO89

AFR1

CMK2

SWI4

RAD16

YER050C

MFA1

STE2 BAR1

MFA2

AGA1 FUS1FUS3

HOG1

FIG1

AGA2

KSS1

RAD6

STE6

RPD3

CRS4

ASG7

CYC8

KAR4

STE11

STE12

STE18

STE24

STE4

STE5

STE7 TUP1

YER044C

SST2

•more information than randomised networks •no optimal •powerlaw distribution of arcs•no obvious modules• local networks make sense

Conclusion

... and now to something completely different

• ChIP• „theoretic“ ChIP= bindingsite network• Gene disruption

Comparison of gene neighbourhoods in graphsFirst, take three types of networks ...

source gene

target geneT1

T2

T3

Target Sets

T1

T2

T3

All genes – target setsAll genes – source sets

s1 T1

s2 T2

T1

T’1

Transcription factors

Disrupted genes

Genes with binding sites for s1

Genes affected by disruption s2s1 s2

s1 = s2

known relationships

target set overlap small

target set overlap large

target set overlap large

predicted relationship

• protein-protein interaction (Y2H, cellzome, etc.)• MIPS (C. v. Mering „reference set“)• Co-citation network (PubMed)

Comparison of gene neighbourhoods in graphs... and three more networks ...

rank for p-value:

1) s1 - s2 - p-value - tp2) s1 - s3 - p-value - fp 3) s1 - s4 - p-value - tp 4) s2 - s3 - p-value - tn5) s2 - s4 - p-value - fn 6) s3 - s4 - p-value - tn

Comparison of gene neighbourhoods in graphs... and then: