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

Transcription

1 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 October 8,

2 Contents l l l l Motivation Modularity and robustness definition Implementation & Results -Analysis of modularity and robustness -Module visualization -Module centrality & GO analysis -Parallel computation of robustness Conclusions 2

3 Motivation Low modularity ( ) High modularity ( ) 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 Complex Systems Computing Lab 3

4 Modularity and robustness definition Complex Systems Computing Lab 4

5 Modularity definition Ø Given a directed graph G(V, E) B A Module V 1 M C w V in wout i Vi Module V 2 M P = ( w V i is1 w w 2 ) [0, 1] M G = max P M(P) Leicht EA, Newman MEJ: Community Structure in Directed Networks. Physical Review Letters 2008, 100(11): Noack A: Modularity clustering is force-directed layout. Physical Review E 2009, 79(2): D G E F P = {V *, V + } Module V 1 : w 89 : {A B, A C, B C} w >?@ 89 :: {C D} w BC 89 :: {G C} Module V 2 : w 8E : {D E, E F, F G, G D, D F, G E} w >?@ 8E :: {G C} w BC 8E :: {C D} M P = ^ ** * * ** + a E ** * * ** E = Complex Systems Computing Lab 5

6 Network robustness definition OR v^ OR v t Initial state original attractor s Inhibit Activate v a v u v + AND AND v u OR Initial state new attractor s vi v v0 v v w * AND OR AND Robustness: γ G = 1 n S k k I( s = s no q r C BS* p ) Complex Systems Computing Lab 6

7 In-/Out- module robustness definition Module V * Module V + In-Module robustness A G B C D E F Calculation of attractor similarity Out-Module robustness γ BC (V * ) I H J Calculation of attractor similarity γ >?@ (V * ) γ BC G = * z γ z BS* BC V B Module V^ γ >?@ G = * z γ z BS* >?@ V B Complex Systems Computing Lab 7

8 Implementation & Results 1. Case study 2. Module visualization 3. Module centrality & GO analysis 4. Parallel computation of robustness Complex Systems Computing Lab 8

9 Signaling networks STKE network: consists of 754 genes and 1,624 interactions HSN network: The human signal transduction network ( consists of 5,443 genes and 37,663 interactions The canonical cell signaling network (STKE network) Complex Systems Computing Lab 9

10 Analysis of modularity and robustness STKE network Modules: 16 Modularity: Robustness: HSN network Modules: 22 Modularity: Robustness: Complex Systems Computing Lab 10

11 Random Boolean network (RBN) model Shuffling interaction model Barabási-Albert (BA) Erdős-Rényi (ER) Erdős-Rényi variant model Scale free network (BA) Erdős-Rényi (ER) Complex Systems Computing Lab 11

12 Analysis of modularity and robustness Generate 6400 RBNs (BA model) and then examine the correlation between modularity and robustness 1 network robustness network modularity random networks HSN STKE Correlation coefficient = with p-value < 10 4 Complex Systems Computing Lab 12

13 Relationship of the network modularity to the in-/outmodule robustness - Relationship of the modularity to the inmodule robustness (R= , p- value <10-4 ). - Modularity and outmodule robustness (not significant). - Network robustness to the in-module robustness (R = , p-value <10-4 ). - Network robustness and out-module robustness (not significant). Complex Systems Computing Lab 13

14 Module visualization (1) Detailed visualization mode A brief mode with absolute relations A brief visualization with relative mode Complex Systems Computing Lab 14

15 Module visualization (2) Ø Results of the detailed visualization mode 16 modules of STKE network 22 modules of HSN network Complex Systems Computing Lab 15

16 Module visualization (3) STKE HSN Absolute mode The reduced visualization results after removing all links except about 30% of links with the highest weight values STKE HSN Complex Systems Computing Lab 16

17 Module visualization (4) STKE HSN Relative mode STKE HSN The reduced visualization results after removing all links except about 30% of links with the highest weight values Complex Systems Computing Lab 17

18 Module centrality analysis How each module is positioned in terms of relations among the modules? Five well-known centrality methods Degree (DEG) Closeness (CLO) Betweeness (BEW) Stress (STR) Eigenvector (EIG) Complex Systems Computing Lab 18

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 Complex Systems Computing Lab 19

20 GO analysis Choose largest module Select the rest of module Ø The interface of GO analysis function in MORO app g/ QuickGO/ Complex Systems Computing Lab 20

21 Parallel computation of robustness 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) Running time (logarithimic scale based 10) Single CPU Multi-core CPU GPU Running mode Running time (logarithimic scale based 10) Single CPU Multi-core CPU GPU Running mode HSN network STKE network Complex Systems Computing Lab 21

22 Some interfaces of MORO Cytoscape app Complex Systems Computing Lab 22

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 ordinary differential equations (ODEs) Complex Systems Computing Lab 23

24 Thank you for your attention! Any question?