Computational Systems Biology

The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful Methods in Molecular Biology'„¢ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete description of a biological system.

Preface	p. v
Contributors	p. xiii
Color Plates	p. xvii
Network Components
Indentification of cis-Regulatory Elements in Gene Co-expression Networks Using A-GLAM	p. 3
Structure-Based Ab Initio Prediction of Transcription Factor-Binding Sites	p. 23
Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations	p. 43
Prediction of Protein-Protein Interactions: A Study of the Co-Evolution Model	p. 61
Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and Issues	p. 89
Prediction and Integration of Regulatory and Protein-Protein Interactions	p. 101
Detecting Hierrchical Modularity in Biological Networks	p. 145
Network Inference
Methods to Reconstruct and Compare Transcriptional Regulatory Networks	p. 163
Learning Global Models of Transcriptional Regulatory Networks from Data	p. 181
Inferring Molecular Interactions Pathways from eQTL Data	p. 211
Methods for the Inference of Biological Pathways and Networks	p. 225
Network Dynamics
Exploring Pathways from Gene Co-expression to Network Dynamics	p. 249
Network Dynamics	p. 269
Kinetic Modeling of Biological Systems	p. 311
Guidance for Data Collection and Computational Modelling of Regulatory Networks	p. 337
Function and Evolutionary Systems Biology
A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure	p. 357
Enzyme Function Prediction with Interpretable Models	p. 373
Using Evolutionary Information to Find Specificity-Determining and Co-evolving Residues	p. 421
Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics	p. 449
Effects of Functional Bias on Supervised Learning of a Gene Network Model	p. 463
Computational Infrastructure for Systems Biology
Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters	p. 479
The Bioverse API and Web Application	p. 511
Computational Representation of Biological Systems	p. 535
Biological Network Inference and Analysis Using SEBINI and CABIN	p. 551
Index	p. 577
Table of Contents provided by Ingram. All Rights Reserved.

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

FREE SHIPPING

Amazon no longer offers textbook rentals. We do!

Computational Systems Biology

Summary

Table of Contents

Supplemental Materials