Yona Introduction to Computational Proteomics

1. Auflage 2010
ISBN: 978-1-4200-1077-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: 0 - No protection

E-Book, Englisch, 767 Seiten

Reihe: Chapman & Hall/CRC Mathematical & Computational Biology

Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis, classification, and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entities, from protein families to interactions, cellular pathways, and gene networks.

The first part of the book presents methods for identifying the building blocks of the protein space, such as motifs and domains. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical models, such as hidden Markov models and support vector machines, that are used to represent protein families and classify new instances.

The second part covers methods that investigate higher order structure in the protein space through the application of unsupervised learning algorithms, such as clustering and embedding. The book also explores the broader context of proteins. It discusses methods for analyzing gene expression data, predicting protein-protein interactions, elucidating cellular pathways, and reconstructing gene networks.

This book provides a coherent and thorough introduction to proteome analysis. It offers rigorous, formal descriptions, along with detailed algorithmic solutions and models. Each chapter includes problem sets from courses taught by the author at Cornell University and the Technion. Software downloads, data sets, and other material are available at biozon.org

Autoren/Hrsg.

Yona, Golan

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

PART I: THE BASICS
What Is Computational Proteomics?
The complexity of living organisms
Proteomics in the modern era
The main challenges in computational proteomics

Basic Notions in Molecular Biology
The cell structure of organisms
It all starts from the DNA
Proteins
From DNA to proteins
Protein folding—from sequence to structure
Evolution and relational classes in the protein space

Sequence Comparison
Alignment of sequences
Heuristic algorithms for sequence comparison
Probability and statistics of sequence alignments
Scoring matrices and gap penalties
Distance and pseudo-distance functions for proteins
Further reading
Conclusions
Appendix: performance evaluation
Appendix: basic concepts in probability

Multiple Sequence Alignment, Profiles, and Partial Order Graphs
Dynamic programming in N dimensions
Classical heuristic methods
MSA representation and scoring
Profile analysis
Iterative and progressive alignment
Transitive alignment
Partial order alignment
Further reading
Conclusions

Motif Discovery
Introduction
Model-based algorithms
Searching for good models: Gibbs sampling and MEME
Combinatorial approaches
Further reading
Conclusions
Appendix: the expectation-maximization algorithm

Markov Models of Protein Families
Introduction
Markov models
Main applications of hidden Markov models (the evaluation and decoding problems)
Learning HMMs from data
Higher order models, codes and compression
Variable order Markov models
Further reading
Conclusions

Classifiers and Kernels
Generative models vs discriminative models
Classifiers and discriminant functions
Applying SVMs to protein classification
Decision trees
Further reading
Conclusions
Appendix

Protein Structure Analysis
Introduction
Structure prediction—the protein folding problem
Structure comparison
Generalized sequence profiles—integrating secondary structure with sequence information
Further reading
Conclusions
Appendix

Protein Domains
Introduction
Domain detection
Learning domain boundaries from multiple features
Testing domain predictions
Multi-domain architectures
Further reading
Conclusions
Appendix

PART II: PUTTING ALL THE PIECES TOGETHER
Clustering and Classification
Introduction
Clustering methods
Vector-space clustering algorithms
Graph-based clustering algorithms
Collaborative clustering
Spectral clustering algorithms
Markovian clustering algorithms
Cluster validation and assessment
Clustering proteins
Further reading
Conclusions
Appendix

Embedding Algorithms and Vectorial Representations
Introduction
Structure preserving embedding
Maximal variance embeddings (PCA, SVD)
Distance preserving embeddings (MDS, random projections)
Manifold learning—topological embeddings (IsoMap, LLE, distributional scaling)
Setting the dimension of the host space
Vectorial representations
Further reading
Conclusions

Analysis of Gene Expression Data
Introduction
Microarrays
Analysis of individual genes
Pairwise analysis
Cluster analysis and class discovery
Enrichment analysis
Protein arrays
Further reading
Conclusions

Protein-Protein Interactions
Introduction
Experimental detection of protein interactions
Prediction of protein-protein interactions
Structure-based prediction, protein docking
Sequence-based inference (gene preservation, co-evolution, sequence signatures, and domain-based prediction)
Topological properties of interaction networks
Network motifs
Further reading
Conclusions
Appendices

Cellular Pathways
Introduction
Metabolic pathways
Pathway prediction
Pathway prediction from blueprints
Expression data and pathway analysis
Regulatory networks and modules
Pathway networks and the minimal cell
Further reading
Conclusions
Bayesian Belief Networks
Introduction
Computing the likelihood of observations
Probabilistic inference
Learning the parameters of a Bayesian network
Learning the structure of a Bayesian network
Further reading
Conclusions
References
Problems appear at the end of each chapter.