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Discovering Biomolecular Mechanisms with Computational Biology

  • Book
  • © 2006

Overview

Authors:
  1. Frank Eisenhaber

    1. Bioinformatics Group, Institute of Molecular Pathology, Vienna, Austria

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  • Contributors examine how sequence analysis becomes even more powerful if it is combined with automated scientific text mining (for the prediction of gene function and gene-disease association), with the analysis of expression data or allele occurrences (single-nucleotide polymorphisms) and frequencies
  • Summarizes non-trivial theoretical predictions for regulatory and metabolic networks that have received experimental confirmation
  • Includes supplementary material: sn.pub/extras

Part of the book series: Molecular Biology Intelligence Unit (MBIU)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xi
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  2. Prediction of Post-translational modifications from amino acid sequence: Problems, pitfalls, methodological hints

    1. Introduction

      • Frank Eisenhaber
      Pages 1-10

  3. Deriving Biological Function of Genome Information with Biomolecular Sequence and Structure Analysis

    1. Front Matter

      Pages 11-11
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    2. Reliable and Specific Protein Function Prediction by Combining Homology with Genomic(s) Context

      • Martijn A. Huynen, Berend Snel, Toni Gabaldón
      Pages 13-29

    3. Clues from Three-Dimensional Structure Analysis and Molecular Modelling

      • Karin Schleinkofer, Thomas Dandekar
      Pages 30-38

    4. Prediction of Protein Function

      • Frank Eisenhaber
      Pages 39-54

  4. Complementing Biomolecular Sequence Analysis with Text Mining in Scientific Articles

    1. Front Matter

      Pages 55-55
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    2. Extracting Information for Meaningful Function Inference through Text-Mining

      • Hong Pan, Li Zuo, Rajaraman Kanagasabai, Zhuo Zhang, Vidhu Choudhary, Bijayalaxmi Mohanty et al.
      Pages 57-73

    3. Literature and Genome Data Mining for Prioritizing Disease-Associated Genes

      • Carolina Perez-Iratxeta, Peer Bork, Miguel A. Andrade
      Pages 74-81

  5. Mechanistic Predictions from the Analysis of Biomolecular Networks

    1. Front Matter

      Pages 83-83
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    2. Model-Based Inference of Transcriptional Regulatory Mechanisms from DNA Microarray Data

      • Harmen J. Bussemaker
      Pages 85-94

    3. The Predictive Power of Molecular Network Modelling

      • Stefan Schuster, Edda Klipp, Marko Marhl
      Pages 95-103

  6. Mechanistic Predictions from the Analysis of Biomolecular Sequence Populations: Considering Evolution for Function Prediction

    1. Front Matter

      Pages 105-105
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    2. Theory of Early Molecular Evolution

      • Edward N. Trifonov
      Pages 107-116

    3. Hitchhiking Mapping

      • Christian Schlötterer
      Pages 117-125

    4. Understanding the Functional Importance of Human Single Nucleotide Polymorphisms

      • Saurabh Asthana, Shamil Sunyaev
      Pages 126-132

    5. Correlations between Quantitative Measures of Genome Evolution, Expression and Function

      • Yuri I. Wolf, Liran Carmel, Eugene V. Koonin
      Pages 133-144

  7. Back Matter

    Pages 145-147
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Keywords

About this book

In this anthology, leading researchers present critical reviews of methods and high-impact applications in computational biology that lead to results that also non-bioinformaticians must know to design efficient experimental research plans. Discovering Biomolecular Mechanisms with Computational Biology also summarizes non-trivial theoretical predictions for regulatory and metabolic networks that have received experimental confirmation.

Discovering Biomolecular Mechanisms with Computational Biology is essential reading for life science researchers and higher-level students that work on biomolecular mechanisms and wish to understand the impact of computational biology for their success.

Authors and Affiliations

  • Bioinformatics Group, Institute of Molecular Pathology, Vienna, Austria

    Frank Eisenhaber

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