Networks in Systems Biology: Applications for Disease Modeling 3030518612, 9783030518615

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

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