ActiveNetworks Cross-Condition Analysis of Functional...

ActiveNetworksCross-Condition Analysis

of Functional Genomic Data

T. M. Murali

April 18, 2006

T. M. Murali April 18, 2006 ActiveNetworks

Motivation: Manual Systems Biology

I Biologists want to study a favourite stress, e.g., oxidativestress or desiccation tolerance.

I Measure gene expression, apply clustering algorithms, and findgenes whose expression level change in response to the stress.

I Trace genes by hand through databases of protein-proteininteractions, gene regulatory networks, metabolic pathways,PubMed searches to build networks activated in response tothe stress.



I Biologists want to study a favourite stress, e.g., oxidativestress or desiccation tolerance.

I Measure gene expression, apply clustering algorithms, and findgenes whose expression level change in response to the stress.

I Trace genes by hand through databases of protein-proteininteractions, gene regulatory networks, metabolic pathways,PubMed searches to build networks activated in response tothe stress.



ARO4

SAH1

YFR055W

SAM1SER3

URA7

LYS14

HIS4 HIS1

lysinesaccharopine

acetoacetate

-KG-ketoglutarate

Thiamine

SAM

transport

PHO3riboswitch ligands?

phospholipid synthesis

SIP18binding

NADPH

HMG-CoA

ERG13

glutamate

acetyl-CoA

acetyl-CoA

YBR238C

FRDS

OSM1

fumaratereductases

osmotic growthprotein

YBL085WTEF4

MET30

SIR3

CYS4

F-box; proteinubiquitination

TPO2 polyamine transport

YHB1 oxidative stress response

LYS12

CLN2

CDC34

GAS3

GAS1

ARO1

S0B L40B

L17B L7A

S26B L27B

S16B L13AS10AS22B

S9B

L7Bribosomalgenes

URA8

serine biosynthesis

cell wallorganization

A

B

C

Redescription R5Heat Shock, 30 min -1 T2 vs T1 -5 AND NOT T2 vs T1 -1

Redescription R5 Gene List

ARO4, ASN1, CLN2, GAS3, HEM13, HIS1, IMD4, PHO3, RPL-7A, 7B, 13A, 17B, 27B, 40B, RPS-0B, 9B, 10A, 16B, 22B, 26B, SAH1, SAM1, SUN4, TEF4, TPO2, URA7, UTR2, YHB1, YBR238C, YER156C, YFR055W, YOR309C

-7 -5 -3 -1Heat shock,

30 minT2 vs. T1

0.71-7 -5 -3 -1

Can we automate this process?



ARO4

SAH1

YFR055W

SAM1SER3

URA7

LYS14

HIS4 HIS1

lysinesaccharopine

acetoacetate

-KG-ketoglutarate

Thiamine

SAM

transport

PHO3riboswitch ligands?

phospholipid synthesis

SIP18binding

NADPH

HMG-CoA

ERG13

glutamate

acetyl-CoA

acetyl-CoA

YBR238C

FRDS

OSM1

fumaratereductases

osmotic growthprotein

YBL085WTEF4

MET30

SIR3

CYS4

F-box; proteinubiquitination

TPO2 polyamine transport

YHB1 oxidative stress response

LYS12

CLN2

CDC34

GAS3

GAS1

ARO1

S0B L40B

L17B L7A

S26B L27B

S16B L13AS10AS22B

S9B

L7Bribosomalgenes

URA8

serine biosynthesis

cell wallorganization

A

B

C

Redescription R5Heat Shock, 30 min -1 T2 vs T1 -5 AND NOT T2 vs T1 -1

Redescription R5 Gene List

ARO4, ASN1, CLN2, GAS3, HEM13, HIS1, IMD4, PHO3, RPL-7A, 7B, 13A, 17B, 27B, 40B, RPS-0B, 9B, 10A, 16B, 22B, 26B, SAH1, SAM1, SUN4, TEF4, TPO2, URA7, UTR2, YHB1, YBR238C, YER156C, YFR055W, YOR309C

-7 -5 -3 -1Heat shock,

30 minT2 vs. T1

0.71-7 -5 -3 -1



Requirements for Automation

I Wiring diagram of the cell: protein-protein interactions,metabolic pathways, transcriptional regulatory networks, . . .

I Measurement of molecular profiles (gene expression, proteinexpression, metabolite levels) under different conditions or cellstates.

I Algorithms for combining these types of information.


High-throughput Biology Provides WiringDiagram

I Large amounts of information on different types of cellularinteractions are now available.

I Protein-protein interactions: genome-scale yeast 2-hybridexperiments, in-vivo pulldowns of protein complexes.

I Transcriptional regulatory networks: ChIP-on-chipexperiments yield protein-DNA binding data.

I Metabolic networks: databases culled from the literature(KEGG).

I Techniques that extract interactions automatically fromabstracts.


S. cerevisiea Wiring Diagram

I Physical networkI 15,429 protein-protein interactions from the Database of

Interacting Proteins (DIP).I 5869 protein-DNA interactions (Lee et al., Science, 2002).I 6,306 metabolic interactions (proteins operate on at least

common metabolite) based on KEGG.

I Genetic networkI 4,125 synthetically lethal/sick interactions (Tong et al.,

Science, 2004).I 687 synthetically lethal interactions (MIPS).

I Overall network has 32,416 (27,604 physical and 4,812genetic) interactions between 5601 proteins (Kelley and Ideker,

Nature Biotech., 2005).


Challenges in Utilising the Wiring Diagram

I Networks are large; they contain tens of thousands ofinteractions.

I High-throughput experiments contain many errors.

I Networks are incomplete; experiments are expensive and havebiases.

I A biologist wants to explore and analyse system of interest.

I How do we zoom into the appropriate parts of the wiringdiagram?


Challenges in Utilising the Wiring Diagram

I Networks are large; they contain tens of thousands ofinteractions.

I High-throughput experiments contain many errors.

I Networks are incomplete; experiments are expensive and havebiases.

I A biologist wants to explore and analyse system of interest.

I How do we zoom into the appropriate parts of the wiringdiagram?


ActiveNetworks

ActiveNetwork: network of interactions activated inresponse to a stress or in a particular condition.

1. Overlay molecular profile for a particular stress on wiringdiagram to obtain ActiveNetwork for that stress.

2. Combine computed ActiveNetworks for each stress tofind

2.1 ActiveNetwork common to multiple stresses.2.2 ActiveNetwork unique to a particular stress or group of

stresses.


ActiveNetworks


1. Overlay molecular profile for a particular stress on wiringdiagram to obtain ActiveNetwork for that stress.



stresses.


The ActiveNetworks pipeline

ActiveNetworksCondition−specific Cross−condition

Act

iveN

etw

ork

s fo

r ea

ch c

ondit

ion

Exper

imen

ts

ActiveNetworks

Hypotheses

interaction networksProtein−protein

pathwaysMetabolic

networks

network

ActiveNetworkmining

computationCarbon ActiveNetwork

Heat shock

Cold shock

Desiccation

starvation

Universal

Transcriptionalregulatory


Overlaying Gene Expression Data

I Weight of an interaction is the Pearson correlation betweenthe expression profiles of the interacting genes.

I Weight ≡ “activity” level of the interaction.

I Discard interactions based on a threshold.I Unsatisfactory since we test each interaction individually.

We find the most highly active subnetwork.




I Weight ≡ “activity” level of the interaction.I Discard interactions based on a threshold.

I Unsatisfactory since we test each interaction individually.





I Weight ≡ “activity” level of the interaction.I Discard interactions based on a threshold.

I Unsatisfactory since we test each interaction individually.



Defining Highly-Active Subnetworks

I How do we measure the activity/weight of a subnetwork?

I Sum or average of edge weights?

I The density of a network with n nodes is the total weight ofthe edges divided by n.

I Problem: Compute the subnetwork with highest density.

0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9







0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9







0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9


Computing Most Dense Subnetwork

I O(n3) time network flow-based approach gives optimal result(Gallo, Grigoriadis, Tarjan, SIAM J. Comp, 1989).

I Can also be solved by linear programming.



I Greedy algorithm:I Weight of a node ≡ total weight of incident edges.I Repeatedly delete nodes with the smallest weight.I Keep track of density of remaining network.I Return the most dense subnetwork.

I Computed subnetwork is at least half as dense as the mostdense subnetwork (Charikar, Proc. APPROX, 2000).

0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9





0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9





0.7

0.4

0.5

0.5

0.3

0.5

0.1

0.80.1

0.9


Computing Multiple Dense Subnetworks

I Repeat

1. Apply greedy algorithm to compute most dense subnetwork.2. Remove edges of computed subnetwork from the network.

I Until remaining network has density less than the originalnetwork.

I Output is a sequence of decreasingly dense subnetworks thatcan share nodes but not edges.


Advantages of Dense Subnetworks

I Uses no parameters.

I Avoid inclusion of interactions that appear active due to noise.

I Relatively weakly correlated interactions can reinforce eachother.


Example of an ActiveNetwork


Example of an ActiveNetwork


Further Analysis of an ActiveNetwork

I Visualise the network (Graphviz package) and the geneexpression profiles.

I Measure functional enrichment.I Use hypergeometric distribution to calculate the significance of

functions enriched in an ActiveNetwork.I Use Bonferroni correction to adjust for testing multiple

hypotheses.


AA Starvation: Conventional Analysis


AA Starvation: ActiveNetwork

SDH1

YLR164W

COR1 RIP1

UGA2

LSC1

QCR10

LSC2

SDH2

IME1

CIN5

PHD1

NRG1

YNL092W

YAP6 MGA1

GPM1

TDH2

TDH3

TDH1

ADH5

PDC5

ADH1

PDC1

PHO11

ADH3

GCN4

GLT1

GLN1

CPA1

CPA2

ADE6

ADE4

GLN4

SIT4

GUA1

MSN2

GID8

HSP104

YKL044W

MSN4

CYB2

PYC1

PYK2

COX5B

ALT1URA8

ELP2IKI1ELP3

KTI12

TKL1

PRS1

PUS1

PRS5

PPT1

PMT4

CKA2

CTT1

PRS3

PRS4


ActiveNetworks for Multiple Stresses


1. Overlay molecular profile for a particular stress on universalnetwork to obtain ActiveNetwork for that stress.



stresses.


ActiveNetworks for Multiple Stresses


1. Overlay molecular profile for a particular stress on universalnetwork to obtain ActiveNetwork for that stress.



stresses.


Comparative ActiveNetwork Analysis

Ex

per

imen

ts

Act

iveN

etw

ork

s fo

r ea

ch c

on

dit

ion

Carbon ActiveNetwork

Universalnetwork

interaction networksProtein−protein Transcriptional

regulatory

starvation

networks

computation

Heat shock

Cold shock

I Richard and Malcolm want to compare desiccationActiveNetwork with other ActiveNetworks to findsimilarities and differences.



Comparative ActiveNetwork Analysis

Act

iveN

etw

ork

s fo

r ea

ch c

on

dit

ion

Ex

per

imen

tsregulatory

network

interaction networks

Universal

Protein−protein

ActiveNetworkcomputation

Carbonstarvation

Heat shock

Cold shock

Desiccation

networks

Transcriptional

I Richard and Malcolm want to compare desiccationActiveNetwork with other ActiveNetworks to findsimilarities and differences.



Cross-Condition ActiveNetworks

Cross−conditionCondition−specificActiveNetworks ActiveNetworks

Act

iveN

etw

ork

s fo

r ea

ch c

ondit

ion

Exper

imen

ts

Carbon

Protein−protein

mining

network

ActiveNetwork


Heat shock

Cold shock

Desiccation

starvation

networks

computationActiveNetwork

interaction networks

Universal

I A “conserved” ActiveNetwork is a set of conditions and aset of interactions, such that each interaction appears in theActiveNetwork for each condition.


Computing Conserved ActiveNetworks

Act

iveN

etw

orks

Interactions

I Construct a 0-1 interaction-by-condition matrix.

I A “conserved” ActiveNetwork is

a set of conditions and aset of interactions

,

such that each interaction appears in theActiveNetwork for each condition.

I We can compute a conserved ActiveNetwork usingtechniques for finding itemsets or biclusters.



Act

iveN

etw

orks

Interactions


I A “conserved” ActiveNetwork is

a set of conditions and aset of interactions

,





Interactions

Act

iveN

etw

orks


I A “conserved” ActiveNetwork is a set of conditions

and aset of interactions

,





Interactions

Act

iveN

etw

orks


I A “conserved” ActiveNetwork is a set of conditions and aset of interactions,





Act

iveN

etw

orks

Interactions






Act

iveN

etw

orks

Interactions






Interactions

Act

iveN

etw

orks

I A “large” submatrix of 1’s is a frequent itemset.

I Such a submatrix is a special case of a bicluster in geneexpression data.

I We use the apriori algorithm for finding all maximal (closed)itemsets (Agrawal and Srikant 1995) and the xMotif algorithmfor finding large biclusters (Murali and Kasif, 2003).



Interactions

Act

iveN

etw

orks

I A “large” submatrix of 1’s is a frequent itemset.

I Such a submatrix is a special case of a bicluster in geneexpression data.

I We use the apriori algorithm for finding all maximal (closed)itemsets (Agrawal and Srikant 1995) and the xMotif algorithmfor finding large biclusters (Murali and Kasif, 2003).


Example of a Cross-Condition ActiveNetwork

RIP1COX12

DLD1

CYB2

QCR6

COR1

QCR8

COX7

HAP4

CYT1

QCR2

I Common to “Alternative carbon sources.” “DTT treatment”and “Growth in YPD culture.”


Example of a Cross-Condition ActiveNetwork

I Common to “Alternative carbon sources.” “DTT treatment”and “Growth in YPD culture.”


Comparative Systems Biology

I ActiveNetworks provide an integrated view ofmulti-modal universal networks and measurements ofmolecular profiles.

I Compute single stimulus ActiveNetworks using densesubgraphs.

I Compare and contrast ActiveNetworks for differentstimuli using frequent itemsets.

I Automatic extraction of network modules and legos from largescale data.

I Promises system-level insights from comparisons betweendifferent conditions, disease states, or species.


Closing the Loop

ActiveNetworksCondition−specific Cross−condition

Act

iveN

etw

ork

s fo

r ea

ch c

on

dit

ion

Ex

per

imen

ts

ActiveNetworks

Hypotheses

interaction networksProtein−protein

pathwaysMetabolic

networks

network

ActiveNetworkmining

computationCarbon ActiveNetwork

Heat shock

Cold shock

Desiccation

starvation

Universal



Project Members

I Greg Grothaus

I Deept Kumar

I Maulik Shukla

I Graham Jack

I Corban Rivera

I Richard Helm

I Malcolm Potts

I Naren Ramakrishnan


Future Research: Modelling and AlgorithmicImprovements

I Integrate other types of data: metabolic measurements,protein expression.

I Explicitly incorporate expression level of a gene.


Future Research: Applications

I ActiveNetworks in cancer: integrate gene expression dataand protein interaction networks.

I Compare oxidative stress networks across kingdom boundaries(yeast, Arabidopsis thaliana, malaria parasite, P. sojae).

I Cross-stress networks in Arabidopsis thaliana.

I Redox signalling in various plant species.


Related Research

I Discovering regulatory and signalling circuits in molecularinteraction networks, Ideker et al. ISMB 2002.

I Physical network models and multi-source data integration, Yeangand Jakkola, RECOMB 2003.

I Discovering molecular pathways from protein interaction and geneexpression data, Segal, Wang, and Koller, ISMB 2003.

I Computational discovery of gene modules and regulatory networks,Bar-Joseph et al., Nature Biotechnology, November 2003.

I Revealing modularity and organization in the yeast molecularnetwork by integrated analysis of highly heterogeneous genomewidedata, Tanay et al., PNAS, March 2004.

I Evidence for dynamically organized modularity in the yeastprotein-protein interaction network, Han et al., Nature, July 2004.

I Genomic analysis of regulatory network dynamics reveals largetoplogical changes, Luscombe et al., Nature, October 2004.


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