Data Management of Protein Interaction Networks - Mario Cannataro - ebook

Data Management of Protein Interaction Networks ebook

Mario Cannataro

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Opis

Current PPI databases do not offer sophisticated queryinginterfaces and especially do not integrate existing informationabout proteins. Current algorithms for PIN analysis use onlytopological information, while emerging approaches attempt toexploit the biological knowledge related to proteins and kinds ofinteraction, e.g. protein function, localization, structure,described in Gene Ontology or PDB. The book discussestechnologies, standards and databases for, respectively,generating, representing and storing PPI data. It also describesmain algorithms and tools for the analysis, comparison andknowledge extraction from PINs. Moreover, some case studies andapplications of PINs are also discussed.

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Liczba stron: 271




Table of Contents

Cover

Series page

Title page

Copyright page

DEDICATION

LIST OF FIGURES

LIST OF TABLES

FOREWORD

PREFACE

WHY READ THIS BOOK NOW?

OUR APPROACH TO INTERACTOMICS

WHO SHOULD READ THIS BOOK?

HOW IS THIS BOOK ORGANIZED?

ACKNOWLEDGMENTS

INTRODUCTION

ACRONYMS

CHAPTER 1 INTERACTOMICS

1.1 INTERACTOMICS AND OMICS SCIENCES

1.2 GENOMICS AND PROTEOMICS

1.3 REPRESENTATION AND MANAGEMENT OF PROTEIN INTERACTION DATA

1.4 ANALYSIS OF PROTEIN INTERACTION NETWORKS

1.5 VISUALIZATION OF PROTEIN INTERACTION NETWORKS

1.6 MODELS FOR BIOLOGICAL NETWORKS

1.7 FLOW OF INFORMATION IN INTERACTOMICS

1.8 APPLICATIONS OF INTERACTOMICS IN BIOLOGY AND MEDICINE

1.9 SUMMARY

CHAPTER 2 TECHNOLOGIES FOR DISCOVERING PROTEIN INTERACTIONS

2.1 INTRODUCTION

2.2 TECHNIQUES INVESTIGATING PHYSICAL INTERACTIONS

2.3 TECHNOLOGIES INVESTIGATING KINETIC DYNAMICS

2.4 SUMMARY

CHAPTER 3 GRAPH THEORY AND APPLICATIONS

3.1 INTRODUCTION

3.2 GRAPH DATA STRUCTURES

3.3 GRAPH-BASED PROBLEMS AND ALGORITHMS

3.4 SUMMARY

CHAPTER 4 PROTEIN-TO-PROTEIN INTERACTION DATA

4.1 INTRODUCTION

4.2 HUPO PSI-MI

4.3 SUMMARY

CHAPTER 5 PROTEIN-TO-PROTEIN INTERACTION DATABASES

5.1 INTRODUCTION

5.2 DATABASES OF EXPERIMENTALLY DETERMINED INTERACTIONS

5.3 DATABASES OF PREDICTED INTERACTIONS

5.4 METADATABASES: INTEGRATION OF PPI DATABASES

5.5 SUMMARY

CHAPTER 6 MODELS FOR PROTEIN INTERACTION NETWORKS

6.1 INTRODUCTION

6.2 RANDOM GRAPH MODEL

6.3 SCALE-FREE MODEL

6.4 GEOMETRIC RANDOM GRAPH MODEL

6.5 STICKINESS INDEX (STICKY) MODEL

6.6 DEGREE-WEIGHTED MODEL

6.7 NETWORK SCORING MODELS

6.8 SUMMARY

CHAPTER 7 ALGORITHMS ANALYZING FEATURES OF PROTEIN INTERACTION NETWORKS

7.1 INTRODUCTION

7.2 ANALYSIS OF PROTEIN INTERACTION NETWORKS THROUGH CENTRALITY MEASURES

7.3 EXTRACTION OF NETWORK MOTIFS

7.4 INDIVIDUATION OF PROTEIN COMPLEXES

7.5 SUMMARY

CHAPTER 8 ALGORITHMS COMPARING PROTEIN INTERACTION NETWORKS

8.1 INTRODUCTION

8.2 LOCAL ALIGNMENT ALGORITHMS

8.3 GLOBAL ALIGNMENT ALGORITHMS

8.4 SUMMARY

CHAPTER 9 ONTOLOGY-BASED ANALYSIS OF PROTEIN INTERACTION NETWORKS

9.1 DEFINITION OF ONTOLOGY

9.2 LANGUAGES FOR MODELING ONTOLOGIES

9.3 BIOMEDICAL ONTOLOGIES

9.4 ONTOLOGY-BASED ANALYSIS OF PROTEIN INTERACTION DATA

9.5 SEMANTIC SIMILARITY MEASURES OF PROTEINS

9.6 THE GENE ONTOLOGY ANNOTATION DATABASE (GOA)

9.7 FUSSIMEG AND PROTEINON

9.8 SUMMARY

CHAPTER 10 VISUALIZATION OF PROTEIN INTERACTION NETWORKS

10.1 INTRODUCTION

10.2 CYTOSCAPE

10.3 CYTOMCL

10.4 NAVIGATOR

10.5 BIOLAYOUT EXPRESS3D

10.6 MEDUSA

10.7 PROVIZ

10.8 ONDEX

10.9 PIVOT

10.10 PAJEK

10.11 GRAPHVIZ

10.12 GRAPHCRUNCH

10.13 VISANT

10.14 PIANA

10.15 OSPREY

10.16 CPATH

10.17 PATIKA

10.18 SUMMARY

CHAPTER 11 CASE STUDIES IN BIOLOGY AND BIOINFORMATICS

11.1 ANALYSIS OF AN INTERACTION NETWORK FROM PROTEOMIC DATA

11.2 EXPERIMENTAL COMPARISON OF TWO INTERACTION NETWORKS

11.3 ONTOLOGY-BASED MANAGEMENT OF PIN (ONTOPIN)

11.4 ONTOLOGY-BASED PREDICTION OF PROTEIN COMPLEXES

CHAPTER 12 FUTURE TRENDS

REFERENCES

Index

Color Plates

Wiley Series on

Bioinformatics: Computational Techniques and Engineering

Bioinformatics and computational biology involve the comprehensive application of mathematics, statistics, science, and computer science to the understanding of living systems. Research and development in these areas require cooperation among specialists from the fields of biology, computer science, mathematics, statistics, physics, and related sciences. The objective of this book series is to provide timely treatments of the different aspects of bioinformatics spanning theory, new and established techniques, technologies and tools, and application domains. This series emphasizes algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology.

Series Editors:     Professor Yi Pan     Professor Albert Y. Zomaya      [email protected]@it.usyd.edu.au

Knowledge Discovery in Bioinformatics: Techniques, Methods and Applications / Xiaohua Hu & Yi Pan

Grid Computing for Bioinformatics and Computational Biology / Albert Zomaya & El-Ghazali Talbi

Analysis of Biological Networks / Björn H. Junker & Falk Schreiber

Bioinformatics Algorithms: Techniques and Applications / Ion Mandoiu & Alexander Zelikovsky

Machine Learning in Bioinformatics / Yanqing Zhang & Jagath C. Rajapakse

Biomolecular Networks / Luonan Chen, Rui-Sheng Wang, & Xiang-Sun Zhang

Computational Systems Biology / Huma Lodhi

Computational Intelligence and Pattern Analysis in Biology Informatics / Ujjwal Maulik, Sanghamitra Bandyopadhyay, & Jason T. Wang

Mathematics of Bioinformatics: Theory, Practice, and Applications / Matthew He & Sergey Petoukhov

Introduction to Protein Structure Prediction: Methods and Algorithms / Huzefa Rangwala & George Karypis

Data Management of Protein Interaction Networks / Mario Cannataro & Pietro Hiram Guzzi

Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.

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Library of Congress Cataloging-in-Publication Data:

Cannataro, Mario, 1964-

 Data management of protein interaction networks / Mario Cannataro, Pietro

Hiram Guzzi.

p. cm. – (Wiley series in bioinformatics ; 17)

ISBN 978-0-470-77040-5 (hardback)

1. Protein-protein interaction–Information resources. 2. Information

resources management. I. Guzzi, Pietro Hiram, 1980- II. Title.

QP551.C346 2012

025.06'572644–dc22

2011010581

eISBN: 9781118103715

oISBN: 9781118103746

ePub: 9781118103739

MOBI: 9781118103722

To Angela, Francesco, and Matteo.

M.C.

To my sister, my mother, my father, and those who are close to me.

P.H.G.

LIST OF FIGURES

1.1   Fragment of the yeast PPI network showing interacting partners of the MCM1 protein. Data are extracted from the MINT database.1.2   Flow of information in interactomics from wet-lab experiments to knowledge.3.1   Modeling friendship relations using graphs. The graph shows friendships among four people: Joey, Johnny, Tommy, and Dede. Joey is a friend of Dede, Tommy, and Johnny; and Dede is a friend of Johnny, Joey, and Tommy.3.2   Example of a graph modeling protein interactions. The graph represents four proteins: A, B, C, and D and the interactions (A, B), (B, C), (B, D), and (C, D).3.3   (a) Undirected and (b) directed Graphs.3.4   Bipartite graph. Red and yellow colors represent, respectively, the V1 and V2 sets.3.5   Undirected graph modeling a simple network.3.6   Graph and its representation as an edge list. Since the graph is undirected, edges are compared only once a time.3.7   Graph and its incidence matrix.3.8   Graph and its adjacency matrix.3.9   Centrality measures.3.10   Node degree as centrality measure. Node colors represent the node degree. Bright colors indicate nodes with a low value of node degree.3.11   Closeness as centrality measure. Bright colors indicate nodes with a low closeness centrality value.3.12   Betweenness as centrality measure. Bright colors indicate nodes with a low centrality value.3.13   Comparison of graph traversal algorithms.4.1   Schema of the PSI-MI XML2.5 file format. The root of a document is represented by an entryset element that contains one or more entries, a self-contained container describing all the interactions, and the related metadata.

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