Evidence for dynamically organized modularity in the yeast protein- protein interaction network Han, et al. 2004.

Slides:

Advertisements

Similar presentations

Classes of complex networks defined by role-to-role connectivity profiles Authors : Roger Guimerà, Marta Sales-pardo, and Luís A. N. Amaral Department.

Advertisements

Mobile Communication Networks Vahid Mirjalili Department of Mechanical Engineering Department of Biochemistry & Molecular Biology.

Network analysis Sushmita Roy BMI/CS 576

Network biology Wang Jie Shanghai Institutes of Biological Sciences.

Putting genetic interactions in context through a global modular decomposition Jamal.

The multi-layered organization of information in living systems

D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April Seminar.

Global Mapping of the Yeast Genetic Interaction Network Tong et. al, Science, Feb 2004 Presented by Bowen Cui.

VL Netzwerke, WS 2007/08 Edda Klipp 1 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Networks in Metabolism.

Biological networks: Types and sources Protein-protein interactions, Protein complexes, and network properties.

Biological networks: Types and sources Protein-protein interactions, Protein complexes, and network properties.

The Network Connection Yasmin Merali

A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles Authors: Chia-Hao Chin 1,4,

University at BuffaloThe State University of New York Young-Rae Cho Department of Computer Science and Engineering State University of New York at Buffalo.

A Real-life Application of Barabasi’s Scale-Free Power-Law Presentation for ENGS 112 Doug Madory Wed, 1 JUN 05 Fri, 27 MAY 05.

Regulatory networks 10/29/07. Definition of a module Module here has broader meanings than before. A functional module is a discrete entity whose function.

27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break.

10/12: “Properties of Interaction Networks”

Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break 14:45 – 15:15Regulatory pathways lecture 15:15 – 15:45Exercise.

Gene and Protein Networks II Monday, April CSCI 4830: Algorithms for Molecular Biology Debra Goldberg.

Biological networks Tutorial 12. Protein-Protein interactions –STRING Protein and genetic interactions –BioGRID Signaling pathways –SPIKE Network visualization.

1 Protein-Protein Interaction Networks MSC Seminar in Computational Biology

WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS

Predicting protein functions from redundancies in large-scale protein interaction networks Speaker: Chun-hui CAI

Graph, Search Algorithms Ka-Lok Ng Department of Bioinformatics Asia University.

MATHEMATICAL PROPERTIES OF NETWORK OF PROTEIN INTERACTIONS

Biological networks: Types and origin

Network analysis and applications Sushmita Roy BMI/CS 576 Dec 2 nd, 2014.

Systems Biology, April 25 th 2007Thomas Skøt Jensen Technical University of Denmark Networks and Network Topology Thomas Skøt Jensen Center for Biological.

Comparative Expression Moran Yassour +=. Goal Build a multi-species gene-coexpression network Find functions of unknown genes Discover how the genes.

Systematic Analysis of Interactome: A New Trend in Bioinformatics KOCSEA Technical Symposium 2010 Young-Rae Cho, Ph.D. Assistant Professor Department of.

Large-scale organization of metabolic networks Jeong et al. CS 466 Saurabh Sinha.

Network Analysis and Application Yao Fu

ANALYZING PROTEIN NETWORK ROBUSTNESS USING GRAPH SPECTRUM Jingchun Chen The Ohio State University, Columbus, Ohio Institute.

LANGUAGE NETWORKS THE SMALL WORLD OF HUMAN LANGUAGE Akilan Velmurugan Computer Networks – CS 790G.

Network Biology Presentation by: Ansuman sahoo 10th semester

Clustering of protein networks: Graph theory and terminology Scale-free architecture Modularity Robustness Reading: Barabasi and Oltvai 2004, Milo et al.

Reconstructing gene networks Analysing the properties of gene networks Gene Networks Using gene expression data to reconstruct gene networks.

Using Graph Theory to Study Neural Networks (Watrous, Tandon, Conner, Pieters & Ekstrom, 2012)

Hubba: Hub Objects Analyzer—A Framework of Interactome Hubs Identification for Network Biology 吳信宏, Hsin-Hung Wu Laboratory.

Part 1: Biological Networks 1.Protein-protein interaction networks 2.Regulatory networks 3.Expression networks 4.Metabolic networks 5.… more biological.

1 Having genome data allows collection of other ‘omic’ datasets Systems biology takes a different perspective on the entire dataset, often from a Network.

Algorithms for Biological Networks Prof. Tijana Milenković Computer Science and Engineering University of Notre Dame Fall 2010.

From High Throughput Pull-Downs To Protein Complexes: Building a Model of the Physical Interactome of Yeast Shoshana Wodak Hospital for Sick Children

CSCE555 Bioinformatics Lecture 18 Network Biology: Comparison of Networks Across Species Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu.

Genome Biology and Biotechnology The next frontier: Systems biology Prof. M. Zabeau Department of Plant Systems Biology Flanders Interuniversity Institute.

Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network Science, Vol 292, Issue 5518, , 4 May 2001.

Discovering functional interaction patterns in Protein-Protein Interactions Networks   Authors: Mehmet E Turnalp Tolga Can Presented By: Sandeep Kumar.

Biological Networks. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002.

Robustness, clustering & evolutionary conservation Stefan Wuchty Center of Network Research Department of Physics University of Notre Dame title.

Informatics tools in network science

1 Lesson 12 Networks / Systems Biology. 2 Systems biology  Not only understanding components! 1.System structures: the network of gene interactions and.

Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network Biology.

Biological networks CS 5263 Bioinformatics.

Departments of Biology and Mathematics

System Structures Identification

Network biology : protein – protein interactions

Assessing Hierarchical Modularity in Protein Interaction Networks

Inferred dynamic model of transcriptional regulatory network.

Department of Computer Science University of York

Modelling Structure and Function in Complex Networks

Integration of omic networks in a developmental atlas of maize

Anastasia Baryshnikova Cell Systems

Towfique Raj, Manik Kuchroo, Joseph M

The protein-protein interaction (PPI) network of four habitats, i. e

Centrality of targeted and neighboring proteins.

Comparison of proteomics and RNA‐Seq data.

Brandon Ho, Anastasia Baryshnikova, Grant W. Brown Cell Systems

Uncovering disease-disease relationships through the incomplete interactome by Jörg Menche, Amitabh Sharma, Maksim Kitsak, Susan Dina Ghiassian, Marc Vidal,

HPV–human protein network map.

Presentation transcript:

Evidence for dynamically organized modularity in the yeast protein- protein interaction network Han, et al. 2004

Preprocessing of PPI data Data sources: Yeast two-hybrid data (5,249) Affinity purifications followed by MS (6,630) Computational predications (7,446) MIPS protein complexes (9,597) FYI (Filtered Yeast Interactome): 2,493 interactions, each observed in at least two different data sources 1,397 proteins A large connected component of 778 proteins

Network of the connected component of 778 proteins

ribosomal protein L23a.e TFIID and SAGA subunit 60S large subunit ribosomal protein L5.e 60S large subunit ribosomal protein L27a.e 60S large subunit ribosomal protein L8.e 60S large subunit ribosomal protein L3.e 60S large subunit ribosomal protein

Power law distribution

37 ribosomal proteins highlighted

Expression correlation Hubs of PPI network Nodes with degree k greater than 5 mRNA expression data set of 315 conditions Five categories of conditions Average PPCs between the hub and each of its respective partners Pearson correlation coefficient

Red curve: average PCCs of hubs Cyan curve: average PCCs of non-hubs Black curve: average PCCS of hubs in randomized networks

Party and date hubs The bimodal distribution suggests that hubs can be split into two distinct populations: One with relatively high average PCCs (108 party hubs) The other with relatively low average PCCs (91 date hubs)

Date hubsParty hubsDate %Party % Total % Regulator451349%12% Adaptor15616%6% Mediator10011%0% Complex168718%81% Other323%2% unclear202%0% Categories according to YPD annotations

Partners of date hubs are significantly more diverse in spatial distribution than partners of party hubs, according to proteome-wide cellular localization data set.

Gradual removal of nodes The characteristic path length, defined as the average distance (shortest path length) between node pairs, reflects the overall network connectivity. Green line: random removal of nodes; Brown line: attack on all hubs; Blue line: attack on party hubs; Red line: attack on date hubs;

Removal of date hubsRemoval of party hubs The largest connected component of the FYI network

Removal of hubs

Sub-networks released by date hub removals are more homogeneous in function

Two types of sub-networks: Stable molecular machines or complexes Loosely connected regulatory pathways

Organized modularity model: date hubs represent global, or ‘higher level, connectors between modules party hubs function inside modules, at a ‘lower level’ of the organization of the proteome.

Genetic perturbations Essentiality GID: genetic interaction density, measures the participation of a protein in genetic interactions.

The End