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Intro; Preface; Foreword to Theoretical and Applied Aspects of Systems Biology; Contents; List of Editors and Contributors; Editors; Contributors; Part I Fundamentals; Bio-modeling Using Petri Nets: A Computational Approach; 1 Modeling Systems Biology; 2 Petri Nets; 2.1 Qualitative Petri Net (QPN); 2.2 Continuous Petri Net (CPN); 2.3 Stochastic Petri Net (SPN); 2.4 Hybrid Petri Net (HPN); 2.5 Colored Petri Net; 3 Model Analysis; 4 Tools; 5 Conclusion and Discussion; References; Modeling Gene Transcriptional Regulation: A Primer; 1 Introduction; 2 Gene Transcriptional Regulation in Prokaryotes
2.1 Basics of Gene Transcriptional Regulation2.2 The Lac Operon; 3 Computational Modeling of Transcriptional Regulation; 3.1 On the Modeling Approach; 3.2 Boolean Logic for the Lac Operon; 3.3 Network Motifs; 4 Discussion; References; Cellular Reprogramming; 1 Introduction; 2 What Is Cellular Reprogramming?; 2.1 Premise; 2.2 Meaning of Cellular Reprogramming; 2.3 Applications; 3 Reprogramming Methods; 3.1 Cellular Reprogramming Through the Overexpression of Transcription Factors; 3.2 Somatic Cell Nuclear Transfer; 3.3 Cell Fusion; 4 Modeling Cellular Reprogramming
4.1 A Data-Oriented Approach4.2 Ordinary Differential Equation; 4.3 Bayesian Network; 4.4 Boolean Network; 5 Cellular Reprogramming Using a Boolean Network; 6 Application of Cellular Reprogramming to Disease Control; 7 Conclusion; References; Metabolic Models: From DNA to Physiology (and Back); 1 Metabolic Models; 2 FBA: Predicting Metabolic Phenotypes; 2.1 Growth Prediction; 2.2 Gene Essentiality; 2.3 Octave Code with FBA Analysis; References; Analysis Methods for Shotgun Metagenomics; 1 Introduction to Metagenomics; 2 Sequence Quality and Identification
2.1 Introduction to Taxonomic Binning2.2 Taxonomic Classification; 2.3 Functional Annotation; 2.4 Normalization; 3 Comparative Analysis; 3.1 Diversity Metrics and Distances; 3.2 Feature Representation and Dimensionality Reduction; 3.2.1 Feature Selection; 3.2.2 Feature Extraction; 3.2.3 Distance-Based Approaches for Feature Extraction; 3.2.4 Neural Network Approaches for Feature Extraction; 4 Diversity Metrics and Constrained Ordination; 5 Statistical Inference; 5.1 Multilevel Regression; 5.2 Multivariate Analysis; 5.3 Differential Abundance Analysis
6 Machine Learning and its Application to Metagenomics6.1 Overview; 6.2 A General Machine Learning Review; 6.3 Taxonomic Classification and DNA Binning; 6.3.1 Naive Bayesian Classifier; 6.3.2 k-Nearest Neighbors; 6.3.3 Clustering; 6.4 Functional Annotation and Prediction; 6.4.1 Hidden Markov Model; 6.4.2 Logistic Regression; 6.5 Phenotype Prediction; 6.5.1 Random Forest; 6.5.2 Support Vector Machine; 6.5.3 Elastic Net; 7 Discussion and Conclusion; References; ANOCVA: A Nonparametric Statistical Test to Compare Clustering Structures; 1 Introduction; 2 The Silhouette Statistic; 3 ANOCVA
2.1 Basics of Gene Transcriptional Regulation2.2 The Lac Operon; 3 Computational Modeling of Transcriptional Regulation; 3.1 On the Modeling Approach; 3.2 Boolean Logic for the Lac Operon; 3.3 Network Motifs; 4 Discussion; References; Cellular Reprogramming; 1 Introduction; 2 What Is Cellular Reprogramming?; 2.1 Premise; 2.2 Meaning of Cellular Reprogramming; 2.3 Applications; 3 Reprogramming Methods; 3.1 Cellular Reprogramming Through the Overexpression of Transcription Factors; 3.2 Somatic Cell Nuclear Transfer; 3.3 Cell Fusion; 4 Modeling Cellular Reprogramming
4.1 A Data-Oriented Approach4.2 Ordinary Differential Equation; 4.3 Bayesian Network; 4.4 Boolean Network; 5 Cellular Reprogramming Using a Boolean Network; 6 Application of Cellular Reprogramming to Disease Control; 7 Conclusion; References; Metabolic Models: From DNA to Physiology (and Back); 1 Metabolic Models; 2 FBA: Predicting Metabolic Phenotypes; 2.1 Growth Prediction; 2.2 Gene Essentiality; 2.3 Octave Code with FBA Analysis; References; Analysis Methods for Shotgun Metagenomics; 1 Introduction to Metagenomics; 2 Sequence Quality and Identification
2.1 Introduction to Taxonomic Binning2.2 Taxonomic Classification; 2.3 Functional Annotation; 2.4 Normalization; 3 Comparative Analysis; 3.1 Diversity Metrics and Distances; 3.2 Feature Representation and Dimensionality Reduction; 3.2.1 Feature Selection; 3.2.2 Feature Extraction; 3.2.3 Distance-Based Approaches for Feature Extraction; 3.2.4 Neural Network Approaches for Feature Extraction; 4 Diversity Metrics and Constrained Ordination; 5 Statistical Inference; 5.1 Multilevel Regression; 5.2 Multivariate Analysis; 5.3 Differential Abundance Analysis
6 Machine Learning and its Application to Metagenomics6.1 Overview; 6.2 A General Machine Learning Review; 6.3 Taxonomic Classification and DNA Binning; 6.3.1 Naive Bayesian Classifier; 6.3.2 k-Nearest Neighbors; 6.3.3 Clustering; 6.4 Functional Annotation and Prediction; 6.4.1 Hidden Markov Model; 6.4.2 Logistic Regression; 6.5 Phenotype Prediction; 6.5.1 Random Forest; 6.5.2 Support Vector Machine; 6.5.3 Elastic Net; 7 Discussion and Conclusion; References; ANOCVA: A Nonparametric Statistical Test to Compare Clustering Structures; 1 Introduction; 2 The Silhouette Statistic; 3 ANOCVA