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MORO: a Cytoscape App for Relationship Analysis between Modularity and Robustness in Large-Scale Biological Networks Authors: Cong-Doan Truong , Tien-Dzung Tran and Yung-Keun Kwon 1 October 8, 2016 Contents l Motivation l Modularity and

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October 8, 2016

MORO: a Cytoscape App for Relationship Analysis between Modularity and Robustness in Large-Scale Biological Networks

Authors: Cong-Doan Truong, Tien-Dzung Tran and Yung-Keun Kwon

1

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Contents

l Motivation l Modularity and robustness definition l Implementation & Results

  • Analysis of modularity and robustness
  • Module visualization
  • Module centrality & GO analysis
  • Parallel computation of robustness

l Conclusions

2

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Complex Systems Computing Lab 3

Motivation

  • Dynamical behaviors, particularly robustness, of biological

networks can be highly affected by their modularity characteristics

  • MORO is Cytoscape app for analyzing relationship

between modularity and robustness

High modularity (0.40518) Low modularity (0.08654)

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Complex Systems Computing Lab 4

Modularity and robustness definition

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Complex Systems Computing Lab 5

Modularity definition

Ø Given a directed graph 𝐻(π‘Š, 𝐹)

Module V1

  • 𝑄 = {π‘Š

*,π‘Š +}

  • Module V1:
  • π‘₯89: {𝐡 β†’ 𝐢, 𝐡 β†’ 𝐷, 𝐢 β†’ 𝐷}
  • π‘₯89

>?@:: {𝐷 β†’ 𝐸}

  • π‘₯89

BC:: {𝐻 β†’ 𝐷}

Module V2

  • Module V2:
  • π‘₯8E: {𝐸 β†’ 𝐹, 𝐹 β†’ 𝐺, 𝐺 β†’ 𝐻, 𝐻 β†’

𝐸, 𝐸 β†’ 𝐺, 𝐻 β†’ 𝐹}

  • π‘₯8E

>?@:: {𝐻 β†’ 𝐷}

  • π‘₯8E

BC:: {𝐷 β†’ 𝐸}

𝑡 𝑸 = βˆ‘ (

𝒙𝑾𝒋 𝒙 βˆ’ 𝒙𝑾𝒋

𝒋𝒐𝒙𝑾𝒋 𝒑𝒗𝒖

π’™πŸ‘

)

𝑡 𝒋S𝟐

∈ [0, 1] 𝑡 𝑯 = π’π’ƒπ’šπ‘Έ 𝑡(𝑸)

  • Leicht EA, Newman MEJ: Community Structure in Directed
  • Networks. Physical Review Letters 2008, 100(11):118703
  • Noack A: Modularity clustering is force-directed layout.

Physical Review E 2009, 79(2):026102

A B C D G F E

  • 𝑁 𝑄 =

^ ** βˆ’ *βˆ—* **E + a ** βˆ’ *βˆ—* **E =

𝟏.πŸ—πŸπŸπŸ•πŸ”

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Complex Systems Computing Lab 6

Network robustness definition

Robustness: 𝛿 𝐻 = 1 π‘œ|𝑇|k k 𝐽( 𝑑 = 𝑑no

p ) C BS* q∈r

𝑀^ 𝑀t

v0

OR OR AND AND OR AND OR AND

Inhibit Activate

𝑀u

𝑀v 𝑀* 𝑀+

𝑀w

𝑀a

𝑀u

01001100

Initial state

  • riginal attractor 𝒕

01001111 11000010

Initial state new attractor π’•π’˜π’‹

01001110 01001010

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Complex Systems Computing Lab 7

In-/Out- module robustness definition

  • 𝛿BC 𝐻 = *

z βˆ‘

𝛿BC π‘Š

B z BS*

  • 𝛿>?@ 𝐻 = *

z βˆ‘

𝛿>?@ π‘Š

B z BS*

π‘π‘π‘’π‘£π‘šπ‘“ π‘Š

^

A B C D F G E

𝛿BC(π‘Š

*)

I H J

𝛿>?@(π‘Š

*)

π‘π‘π‘’π‘£π‘šπ‘“ π‘Š

*

π‘π‘π‘’π‘£π‘šπ‘“ π‘Š

+

B 1101100110 1010101110 0001101110 100 001 000

Calculation of attractor similarity

G 1100110 1100110 1001011

Calculation of attractor similarity

1001100110 1001100110 0011100110

  • In-Module robustness

1001101110 0101001011

  • Out-Module robustness
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Complex Systems Computing Lab 8

1. Case study 2. Module visualization 3. Module centrality & GO analysis 4. Parallel computation of robustness

Implementation & Results

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Complex Systems Computing Lab 9

Signaling networks

  • STKE network:

– consists of 754 genes and 1,624 interactions – http://stke.sciencemag.org

  • HSN network:

– The human signal transduction network (www.bri.nrc.ca/wang) – consists of 5,443 genes and 37,663 interactions

The canonical cell signaling network (STKE network)

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Complex Systems Computing Lab 10

Analysis of modularity and robustness

  • STKE network
  • Modules: 16
  • Modularity: 0.72825
  • Robustness: 0.67721
  • HSN network
  • Modules: 22
  • Modularity: 0.54534
  • Robustness: 0.75400
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Complex Systems Computing Lab 11

Random Boolean network (RBN) model

  • Shuffling interaction model
  • BarabΓ‘si-Albert (BA)
  • ErdΕ‘s-RΓ©nyi (ER)
  • ErdΕ‘s-RΓ©nyi variant model

Scale free network (BA) ErdΕ‘s-RΓ©nyi (ER)

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Complex Systems Computing Lab 12

Analysis of modularity and robustness

  • Generate 6400 RBNs (BA model) and then examine the

correlation between modularity and robustness

0.2 0.4 0.6 0.8 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 network robustness network modularity random networks HSN STKE

Correlation coefficient = βˆ’0.80303 with p-value < 10βˆ’4

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Complex Systems Computing Lab 13

Relationship of the network modularity to the in-/out- module robustness

  • Relationship of the

modularity to the in- module robustness (R= βˆ’0.30383, p- value <10-4).

  • Modularity and out-

module robustness (not significant).

  • Network robustness

to the in-module robustness (R = 0.27801, p-value <10-

4).

  • Network robustness

and out-module robustness (not significant).

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Complex Systems Computing Lab 14

Module visualization (1)

  • Detailed visualization mode
  • A brief mode with absolute relations
  • A brief visualization with relative mode
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Complex Systems Computing Lab 15

Module visualization (2)

16 modules of STKE network 22 modules of HSN network

Ø Results of the detailed visualization mode

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Complex Systems Computing Lab 16

Module visualization (3)

Absolute mode The reduced visualization results after removing all links except about 30% of links with the highest weight values STKE HSN STKE HSN

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Complex Systems Computing Lab 17

Module visualization (4)

Relative mode The reduced visualization results after removing all links except about 30%

  • f links with

the highest weight values HSN STKE STKE HSN

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Complex Systems Computing Lab 18

Module centrality analysis

  • Five well-known centrality methods
  • Degree (DEG)
  • Closeness (CLO)
  • Betweeness (BEW)
  • Stress (STR)
  • Eigenvector (EIG)

How each module is positioned in terms of relations among the modules?

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Complex Systems Computing Lab 19

Module centrality result

Ø The correlation between five centrality values and module sizes of STKE network The module size which is defined as the number of nodes belonging to the module showed positive relationships with all module centrality measures

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Complex Systems Computing Lab 20

GO analysis

Choose largest module Select the rest

  • f module

Ø The interface of GO analysis function in MORO app

  • http://www.uniprot.or

g/

  • http://www.ebi.ac.uk/

QuickGO/

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Complex Systems Computing Lab 21

Parallel computation of robustness

1 10 100 1000 10000 Single CPU Multi-core CPU GPU

Running time (logarithimic scale based 10) Running mode

0.01 0.1 1 10 100 Single CPU Multi-core CPU GPU

Running time (logarithimic scale based 10) Running mode

Ø Running time of MORO based on three modes such as single CPU, Multi-core CPU and GPU) with number of considered initial-states (1000)

HSN network STKE network

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Complex Systems Computing Lab 22

Some interfaces of MORO Cytoscape app

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Complex Systems Computing Lab 23

Conclusions

  • Summary:

– Analyze the relationship between network robustness and modularity – Provide various module visualization modes – Analyze module centrality by employing five well-known methods – Analyze gene ontology of two groups of modules – Implement robustness algorithms in parallel – Provide a batch-mode simulation

  • Future works:

– Consider various types of mutations such as a knockout and edge mutation – Extend Boolean network model by using

  • rdinary

differential equations (ODEs)

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Thank you for your attention! Any question?