Discovering biomolecular mechanisms with computational biology

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.

Frank EisenhaberMartijn A. Huynen and Berend Snel and Toni GabaldonKarin Schleinkofer and Thomas DandekarFrank EisenhaberHong Pan and Li Zuo and Rajaraman Kanagasabai and Zhuo Zhang and Vidhu Choudhary and Bijayalaxmi Mohanty and Sin Lam Tan and S.P.T. Krishnan and Pardha Sarathi Veladandi and Archana Meka and Weng Keong Choy and Sanjay Swarup and Vladimir B. BajicCarolina Perez-Iratxeta and Peer Bork and Miguel A. AndradeHarmen J. BussemakerStefan Schuster and Edda Klipp and Marko MarhlEdward N. TrifonovChristian SchlottererSaurabh Asthana and Shamil SunyaevYuri I. Wolf and Liran Carmel and Eugene V. Koonin

Introduction Bioinformatics: Mystery, Astrology or Service Technology?	p. 1
Mystery	p. 2
Astrology	p. 6
Service Technology	p. 7
Section I Deriving Biological Function of Genome Information with Biomolecular Sequence and Structure Analysis
1 Reliable and Specific Protein Function Prediction by Combining Homology with Genomic(s) Context	p. 13
Types of Genomic Context	p. 14
Accuracy and Genomic Coverage of Context Based Predictions	p. 16
Experimentally Verified Context Predictions	p. 17
Practical Examples of New Protein Function Predictions Based on Genomics Data	p. 18
Discussion	p. 25
2 Clues from Three-Dimensional Structure Analysis and Molecular Modelling: New Insights into Cytochrome P450 Mechanisms and Functions	p. 30
Modelling	p. 31
Molecular Dynamic Simulation	p. 35
3 Prediction of Protein Function: Two Basic Concepts and One Practical Recipe	p. 39
The Beginning: Deriving the Protein Sequence and the Definition of Protein Function	p. 40
Concept No. 1: Function Inheritance from a Common Ancestor Gene	p. 40
Limitations of the Homology Search Concept	p. 41
Concept No. 2: Lexical Analysis, Physical Interpretation and Sequence Motif-Function Correlations	p. 42
A Recipe for Analyzing Protein Sequences	p. 43
Section II Complementing Biomolecular Sequence Analysis with Text Mining in Scientific Articles
4 Extracting Information for Meaningful Function Inference through Text-Mining	p. 57
Scope and Nature of Text-Mining in Biomedical Domain	p. 58
Focus of Our Text-Mining Systems	p. 59
Supporting Text-Mining Systems for Gene Regulatory Networks Reconstruction	p. 59
Dragon TF Relation Extractor (DTFRE)	p. 60
Rulebase Construction	p. 61
Rule Representation	p. 62
Learning Algorithm	p. 62
Mining Associations of Transcription Factors by Dragon TF Association Miner	p. 63
Exploring Metabolome of Arabidopsis thaliana and Other Plant Species by Dragon Metabolome Explorer	p. 65
Comparative Analysis of Bacterial Species	p. 69
5 Literature and Genome Data Mining for Prioritizing Disease-Associated Genes	p. 74
Mapping Symptoms to Gene Functions	p. 75
Sequence Homology Based Searches	p. 76
Performance of the System	p. 77
Examples on How the System Works	p. 78
Limitations, Scope, and Further Directions	p. 79
The G2D Web Server	p. 81
Section III Mechanistic Predictions from the Analysis of Biomolecular Networks
6 Model-Based Inference of Transcriptional Regulatory Mechanisms from DNA Microarray Data	p. 85
Two Classes of Tools for Finding Motifs from Expression Data	p. 86
Reduce: Motif-Based Regression Analysis of the Transcriptome	p. 86
From Central Dogma to "Omes Law"	p. 87
Three Different Ways of Using Regression Analysis	p. 88
MA-Networker: Integrating Occupome and Transcriptome Data	p. 89
7 The Predictive Power of Molecular Network Modelling: Case Studies of Predictions with Subsequent Experimental Verification	p. 95
Optimal Time Course of Gene Expression	p. 97
A Previously Unknown Metabolic Pathway	p. 99
Decoding of Calcium Oscillations	p. 100
Discussion	p. 101
Section IV Mechanistic Predictions from the Analysis of Biomolecular Sequence Populations: Considering Evolution for Function Prediction
8 Theory of Early Molecular Evolution: Predictions and Confirmations	p. 107
Consensus Temporal Order of Amino Acids	p. 109
Reconstruction of the Origin and Evolution of the Triplet Code	p. 109
Prediction I Early Proteins Were Glycine-Rich	p. 110
Prediction I The Earliest Protein Sequences Were a Mosaic of Two Independent Alphabets	p. 112
Prediction III Fundamental Binary Code of Protein Sequences	p. 112
Prediction IV Domestication of Life?	p. 114
Linking the Codon Chronology with Other Events of Early Evolution	p. 115
9 Hitchhiking Mapping: Limitations and Potential for the Identification of Ecologically Important Genes	p. 117
Microsatellites-A Widely Used Genetic Marker	p. 118
The InRq Statistic	p. 118
Mapping the Target of Selection	p. 119
10 Understanding the Functional Importance of Human Single Nucleotide Polymorphisms	p. 126
Comparative Sequence Analysis	p. 127
Structure-Based Methods	p. 128
Combined Methods	p. 128
Methods of Validation	p. 129
Detecting the Effects of Selection	p. 129
Genome-Wide Analysis of Functional Polymorphic Variants	p. 130
Significance for Medical Genetics	p. 130
11 Correlations between Quantitative Measures of Genome Evolution, Expression and Function	p. 133
Evolution Rate, Expression Level and Expression Breadth	p. 135
Evolution Rate, Gene Loss and Fitness Effect	p. 135
Gene Duplications and Evolution Rate	p. 137
Interactions between Three and More Parameters: More Than the Sum of the Parts?	p. 138
The "Social Status" Model	p. 138
Multi-Dimensional Structure of Expression, Evolution Rate, and Gene Loss Data	p. 140
Appendix 1 Mutual Entropy of Gene Knockout Data and Phyletic Patterns	p. 142
Appendix 2 Expression Level, Evolution Rate, and Gene Loss as Predictors of Viability of Gene Knockout Mutants	p. 142
Index	p. 145

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