Table of contents : Foreword Preface Contents Part IBiological Networks and Methods in Systems Biology 1 Network Medicine: Methods and Applications 1.1 Introduction 1.1.1 Basic Concepts in Graph Theory 1.2 Biological Networks 1.2.1 Protein–Protein Interaction (PPI) Networks 1.2.2 Gene Regulatory Networks 1.2.3 Metabolic Networks 1.2.4 Genetic Interaction Networks 1.2.5 Pathogen–Host Interactomes 1.3 Biological Networks for Functional Annotations of Proteins and Complexes 1.4 Biological Networks and Diseases 1.4.1 Disease Genes and Subnetworks 1.4.2 Disease Networks 1.5 Biological Networks and Drugs References 2 Computational Tools for Comparing Gene Coexpression Networks 2.1 Introduction 2.2 Network Comparison Methods 2.2.1 Edge Comparison 2.2.2 Untargeted Vertex Comparison 2.2.3 Targeted Vertex Comparison 2.3 Conclusion References 3 Functional Gene Networks and Their Applications 3.1 Introduction 3.2 Functional Networks at the Gene Level 3.2.1 The Bayesian Approach for Building Functional Gene Networks 3.2.2 Description of Established Functional Gene Networks 3.3 Functional Gene Networks at the Isoform Level 3.3.1 Alternative Splicing 3.3.2 Methods for Building Functional Isoform Networks 3.3.3 Functional Isoform Networks for Humans 3.3.4 Functional Isoform Networks for Mice 3.4 Conclusion References 4 A Review of Artificial Neural Networks for the Prediction of Essential Proteins 4.1 Introduction 4.2 Background 4.2.1 Essentiality as a Classification Problem 4.2.2 Artificial Neural Networks 4.3 Research Scenario 4.3.1 Research Works 4.3.2 Considerations 4.4 Conclusion References 5 Transcriptograms: A Genome-Wide Gene Expression Analysis Method 5.1 Introduction 5.2 The Method 5.2.1 The Gene Ordering 5.2.2 Transcriptograms 5.2.3 The Biological Meaning of Transcriptograms 5.2.4 Statistical Tests 5.2.5 Noise Reduction 5.3 Case Studies 5.3.1 Saccharomyces cerevisiae: Cell Cycle 5.3.2 ADPKD: Therapy Target Identification 5.4 Quality Control and Normalization Quality Assessment 5.5 Transcriptogram Softwares 5.5.1 Transcriptogramer R/Bioconductor Package References 6 A Tutorial on Sobol' Global Sensitivity Analysis Applied to Biological Models 6.1 Introduction 6.2 Mathematical Modeling 6.3 Sensitivity Analysis 6.3.1 Sobol' Indices 6.4 Surrogate Models 6.4.1 Polynomial Chaos Expansion 6.4.2 Calculation of the Coefficients 6.4.3 Surrogate Error Estimation 6.4.4 PCE-Based Sobol' Indices 6.5 A Practical Tutorial 6.5.1 Tutorial Description 6.5.2 SoBioS: Sobol' Indices for Biological Systems 6.5.3 Example 1: Predator–Prey Dynamics 6.5.4 Example 2: NF-κB Signaling Pathway 6.5.5 Example 3: The SIR Model 6.6 Final Remarks References 7 Reaction Network Models as a Tool to Study Gene Regulation and Cell Signaling in Development and Diseases 7.1 Introduction 7.2 Gene Regulation and Cell Signaling 7.2.1 NFkB Signaling Pathway 7.3 Dynamic System Theory 7.3.1 Fixed Points and Stability 7.3.2 Steady-State Analysis 7.3.3 Bifurcation 7.3.4 Bistability and Oscillation 7.3.5 Bistability: A Practical Example 7.4 Parameter Estimation Strategies 7.4.1 Formal Problem Definition 7.4.2 Parameter Estimation Methods 7.4.3 Constrained Optimization 7.4.4 Software Availability 7.5 Bistability in Developmental Biology 7.5.1 A System Biology Approach 7.5.2 Modeling Drosophila Embryonic Development 7.5.3 Modeling the Expression of the Hunchback Gene 7.5.4 The Expression Pattern of a Developmental Gene: A Practical Example References Part IIDisease and Pathogen Modeling 8 Challenges for the Optimization of Drug Therapy in the Treatment of Cancer 8.1 The Personalized Medicine of Cancer 8.1.1 Benefits of Personalized Oncology 8.1.2 Personalized Cancer Therapies 8.1.3 OMIC Tests 8.1.4 Drugs 8.1.5 Preclinical Trial 8.1.6 Clinical Trial 8.1.7 Survival 8.1.8 Regulation 8.1.9 Costs 8.2 What the Molecular Phenotype Can Tell Us? 8.2.1 Tumor Modeling 8.3 Drug Development References 9 Opportunities and Challenges Provided by Boolean Modelling of Cancer Signalling Pathways 9.1 Background 9.2 Methodology 9.3 Application of Boolean Modelling to Oncogenic Pathways 9.4 Boolean Dynamics in Cancer Signalling 9.4.1 Leukaemia 9.4.2 Colon Cancer 9.4.3 Prostate Cancer 9.4.4 Breast Cancer 9.4.5 Other Cancer Types 9.5 Discussion References 10 Integrating Omics Data to Prioritize Target Genes in Pathogenic Bacteria 10.1 Introduction 10.2 How to Prioritize Targets in Pathogenic Bacteria? 10.2.1 Metabolic Network Modeling 10.2.2 Transcriptional Regulatory Network (TRN) Modeling 10.2.3 Integrating Genome-Scale Models (GSMs) 10.2.4 Structural Information at the Genomic Scale 10.2.5 Web Servers for Target Selection in Pathogens 10.3 Pathogen-Focused Applications 10.3.1 Staphylococcus aureus 10.3.2 Klebsiella pneumoniae 10.3.3 Mycobacterium tuberculosis 10.4 Conclusions and Perspectives References 11 Modelling Oxidative Stress Pathways 11.1 Introduction 11.2 Protein–Protein Interaction Networks (PPIN) 11.2.1 Interaction Databases 11.2.2 The Problem of Redundant Interactions 11.2.3 Interaction Reliability 11.2.4 Generating Novel Interactions 11.2.5 Network Construction 11.2.6 Dynamic Interaction Networks 11.2.7 Network Analysis 11.3 Flux Balance Analysis (FBA) 11.3.1 Metabolic Reconstruction 11.3.2 Construction of the Stoichiometric Matrix 11.3.3 Defining an Objective Function 11.3.4 Flux Balance Analysis Tools 11.4 Boolean Networks 11.4.1 Network Construction 11.4.2 Network Analysis Tools 11.4.3 A Simplified Example of a Boolean Network 11.5 Centrality and Clustering in Biological Networks 11.6 High-Throughput and Omic-Based Screening Methods and Their Application in Systems Biology 11.6.1 Transcriptomics 11.6.2 Proteomics and the Redox Proteome 11.6.3 Secretomics 11.6.4 Lipidomics 11.6.5 Metabolomics—Biomarkers and Mechanisms 11.7 Machine Learning in Systems Biology 11.7.1 Machine Learning and the Redox Proteome 11.7.2 Machine Learning, Multiomics Data and FBA 11.7.3 Machine Learning and Network Biology 11.8 Concluding Remarks References 12 Computational Modeling in Virus Infections and Virtual Screening, Docking, and Molecular Dynamics in Drug Design 12.1 Computational Modeling in Viral Infections 12.1.1 Viral Vectors 12.1.2 Virus-like Particles 12.1.3 Pharmaceutical Bioprocess 12.1.4 Papillomavirus 12.1.5 Hepatitis B Virus (HBV) 12.1.6 Hepatitis C Virus (HCV) 12.1.7 Coronavirus 12.1.8 Zika Virus 12.2 Virtual Screening in Drug Design 12.2.1 Computer-Aided Drug Design (CADD) 12.2.2 The Virtual Screening Process 12.2.3 Repurposing of Drugs 12.3 Molecular Docking in Drug Design 12.3.1 Theory of Molecular Docking 12.3.2 Challenges of Molecular Docking 12.4 Molecular Dynamics (MD) 12.4.1 Force Fields 12.4.2 MD Simulations 12.4.3 Analysis of MD Simulations 12.4.4 MD Applications References 13 Cellular Regulatory Network Modeling Applied to Breast Cancer 13.1 Introduction 13.1.1 Background in Gene Regulatory Networks Modeling 13.1.2 Cellular Activity Regulation and Cancer Biology Aspects 13.2 Methodology for Building Gene Regulatory Networks Models 13.2.1 Network Characterization 13.2.2 Reference Model to Construct Boolean Networks 13.2.3 Reduced Network Topology 13.2.4 Building a Simplified Target-Network 13.3 Results of Simplified Target-Network Construction 13.3.1 Binarization of Gene Expression Data 13.3.2 Parameters Impact 13.3.3 Choice of the Boolean Function Used in Simulations 13.3.4 Simulations and Search for Attractors and Steady States 13.4 Conclusions References Index