Reverse Engineering of Regulatory Networks: Methods and Protocols (Methods in Molecular Biology, 2719)

✍ Scribed by Sudip Mandal (editor)

• Publisher: Humana
• Year: 2023
• Tongue: English
• Leaves: 331
• Category: Library

✦ Synopsis

This volume details the development of updated dry lab and wet lab based methods for the reconstruction of Gene regulatory networks (GRN). Chapters guide readers through culprit genes, in-silico drug discovery techniques, genome-wide ChIP-X data, high-Throughput Transcriptomic Data Exome Sequencing, Next-Generation Sequencing, Fuorescence Spectroscopy, data analysis in Bioinformatics, Computational Biology, and S-system based modeling of GRN. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Reverse Engineering of Regulatory Networks aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge.

✦ Table of Contents

Preface
Contents
Contributors
Chapter 1: Molecular Modeling Techniques and In-Silico Drug Discovery
1 Introduction
1.1 Building the Structures of Molecules
1.2 Binding Interactions Between Molecules and Virtual Screening of Ligand Libraries
2 Methods
2.1 Building the Structures of Molecules
2.1.1 Analysis of the Primary Structure of the Target
2.1.2 Identification of the Suitable Template for the Target
2.1.3 Building the Model of the Target
2.1.4 Assessment the Quality of the Built Model
MODELLER
2.2 Virtual Screening of Ligand Libraries
3 Notes
4 Conclusion
References
Chapter 2: Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of ...
1 Introduction
2 Materials
2.1 Databases
2.2 Software
3 Methods
3.1 Microarray Dataset
3.2 Data Processing and Identification of DEGs
3.3 Functional Annotation and KEGG Pathway for DEGs
3.4 Protein-Protein Interactions Analysis
3.5 Identification of Hub Genes
3.6 Construction of the TF-miRNA-target Gene Regulatory Network
3.7 Identification of Protein-Drug Interactions
3.8 Hub Gene Survival and Expression Level Analysis
4 Interpretation of Results
4.1 Gene Expression Analysis
4.2 Pathway and Functional Association Analysis
4.3 Protein-Protein Interactions (PPIs) Analysis
4.4 Hub Proteins Were Identified from Protein-Protein Interaction Analysis
4.5 TF-miRNA Target DEG Regulatory Network Analysis
4.6 Identification of Protein-Drug Interactions
4.7 The Expression Level and Hub Genes Kaplan-Meier Plotter
5 Notes
6 Conclusion and Outlook
References
Chapter 3: Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures
1 Introduction
2 Materials
2.1 Principle of Fluorescence Spectroscopy
2.2 Instrumentation of Fluorescence Spectroscopy
2.3 Materials and Sample Preparation
3 Methods
3.1 Steady-state Fluorescence Spectroscopy and Quenching
3.2 Time-Resolved Fluorescence Decay Measurements
3.3 Time-Resolved Fluorescence Anisotropy Decay Measurements
3.4 Fluorescence Correlation Spectroscopy
3.5 FRET and Single-Molecule Fluorescence Spectroscopy
3.6 Information Extraction of Fluorescence Spectra
4 Notes
References
Chapter 4: Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data
1 Introduction
2 Materials
3 Methods
3.1 RNA-SeqData Acquisition
3.2 Quality Control of Raw Reads
3.3 Reads Pre-processing
3.4 Read Alignment Against the Reference Genome
3.5 Read Assignment
3.6 Differential Gene Expression
3.7 Reconstruction of Growth Regulatory Network (GRN)
3.8 Network Visualization and Inference
3.8.1 Integration of Publicly Available Interaction Data
3.8.2 Import Network to Cytoscape
3.8.3 Topological Inference
3.9 Functional Annotation of Deregulated Genes
3.10 Final Remarks
4 Notes
References
Chapter 5: Implementation of Exome Sequencing to Identify Rare Genetic Diseases
1 Introduction
2 Materials
3 Methods
3.1 DNA Extraction
3.1.1 Phenol-Chloroform Extraction of Genomic DNA from Peripheral White Blood Cells
3.1.2 gDNA Extraction with Kits from Commercial Suppliers
3.2 Exome Library Preparation
3.2.1 DNA Fragmentation
3.2.2 Library Construction and Clean-up
3.2.3 End Adenylation (A-tailing)
3.2.4 Adapter Ligation
3.2.5 Target Enrichment
3.3 Variant Annotation
3.4 Variant Calling and Annotation
3.5 Variant Prioritization
3.6 In silico Analysis
3.7 Applications
4 Notes
5 Conclusion
References
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Chapter 6: Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Stud...
1 Introduction
1.1 Big Data Resource Challenges and Promises
1.1.1 Genomic Database Resources
1.1.2 Transcriptomics Big Database Resource
1.1.3 Proteomics Database Resources
1.1.4 Metabolomics Database Resources
1.1.5 Biological Pathway Database Resource
1.2 A Case Study of Epilepsy and Seizures
2 Materials
2.1 DisGeNET Database
2.2 GeneMANIA Prediction Server
2.3 NetworkAnalyst
2.4 MCODE Plugin
2.5 Cytoscape Software
2.6 FunRich Tool
3 Methods
3.1 Generation of Gene-disease-variant-Associated Network
3.2 Genetic Interaction Network
3.3 Cluster Analysis of the Regulatory Network
3.4 Gene-mRNA-TFs Regulatory Network
3.5 Gene Ontology Analysis
4 Notes
References
Chapter 7: New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the ...
1 Introduction
2 Materials
2.1 DisGeNET Database
2.2 GEIO2R Tool
2.3 Reactome FIViz Plugins
2.4 Cystoscope Software
2.5 BiNGO
3 Methods
3.1 Gene Disease Association Network
3.2 Pathway Enrichment Analysis
3.3 Functional Interaction (FI) Network
3.4 Network Enrichment Analysis
4 Notes
References
Chapter 8: A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network
1 Introduction
2 History of S-system
3 S-system Based Modeling of GRN
3.1 Preliminary of S-system-based GRN
3.2 Few Major Issues Regarding Optimization for S-system Parameters
3.2.1 Major Issue 1: Computational Complexity
3.2.2 Major Issue 2: Accuracy in the Prediction of Dynamics of Genes
3.2.3 Major Issue 3: Over-fitting Problem
3.3 Proposed Solutions Regarding the Above Issues
3.3.1 Decoupling to Reduce Computational Complexity
3.3.2 Selection of Suitable Optimization Technique to Increase Accuracy
3.3.3 Regularization to Deal with Over-fitting Problem
3.4 How to Validate a New Algorithm for S-system-based GRN Reconstruction?
4 Literature Survey
5 Conclusion
References
Chapter 9: Big Data in Bioinformatics and Computational Biology: Basic Insights
1 Introduction
1.1 Importance of Big Data in Biology
1.2 Big Data Handling: Collection, Storage, and Analysis
2 Tools/Softwares
3 Methods
3.1 Data Collection
3.2 Data Storage
3.2.1 Rules to Store Data
3.2.2 Data Storage Systems
3.2.3 Important Features of a Big Data Database
3.3 Data Analysis
3.3.1 Descriptive Analysis
Analysis of Sequence Data
Gene Expression Analysis
3.3.2 Predictive Analysis
Supervised Learning
Unsupervised Learning
4 Big Data Solutions: The Data Architectures
4.1 MapReduce Architecture
4.2 Fault Tolerance Architecture
4.3 Stream Graph Architecture
5 Conclusion
References
Chapter 10: Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data
1 Introduction
2 Materials
2.1 Dataset
2.2 Software
3 Methods
3.1 Package Installation
3.2 Importing Raw Data
3.3 Quality Control of Raw Data
3.4 Normalization and QC (RMA Method)
3.4.1 PCA Plot for QC
3.5 Differential Expression Analysis
3.6 Heatmap
3.7 Annotation
3.8 Gene Ontology (GO) and Pathway Enrichment (KEGG) Analysis
4 Notes
5 Conclusion
References
Chapter 11: Big Data Analysis in Computational Biology and Bioinformatics
1 Introduction
1.1 Data Acquisition and Storage
1.2 Data Processing and Analysis
1.3 Tools to Handling Big Data Analysis in Computational Biology and Bioinformatics
1.4 Linux Shell Scripts
1.5 Hadoop
1.5.1 Hadoop Modules
1.5.2 Hadoop´s Working Mechanism
1.6 R-Programming Language
1.6.1 Data Preprocessing
1.6.2 Data Analysis
1.6.3 Data Visualization
1.6.4 Data Storage
1.7 Python Programming Language
2 Review of Literature
3 Challenges and Opportunities
4 Conclusion
References
Chapter 12: Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Program...
1 Introduction
2 Materials
3 Methods
3.1 Obtaining Upstream Sequences of a Gene from the Genome Sequence
3.2 Finding Enrichment of TFBS Motifs in a Single Sequence: A Case Study of the EOMES Gene
3.3 Exploratory Analysis of Sequence Report
3.4 Examination and Visualization of Significant TFBS Motifs in the EOMES Promoter
3.5 Validating Significant TFBS Motifs in the EOMES Promoter Against Chance
3.6 Understanding and Interpretating Results in Functional Contexts
3.7 Pointers on Exporting Results
3.8 Finding Motif Enrichment in a Group of Genes
3.9 Limitations
4 Notes
References
Chapter 13: Hubs and Bottlenecks in Protein-Protein Interaction Networks
1 Introduction
1.1 Methods for Identifying Protein-Protein Interactions
1.2 Protein-Protein Interaction Databases
1.3 Protein-Protein Interaction Networks
2 Centrality Measures
3 Characteristics Features of Hubs
3.1 Dichotomy Among Hubs
4 Characteristics Features of Bottlenecks
5 Further Categories Among Hubs and Bottlenecks
6 Summary
References
Chapter 14: Next-Generation Sequencing to Study the DNA Interaction
1 Introduction
2 Sequencing
3 Sanger Sequencing Versus NGS
4 Roche 454 Sequencing Technique
5 SOLiD ABI Platform
6 Illumina (Solexa) Sequencing Platform
7 Other New Sequencing Technologies
7.1 Polony-Based Sequencing Technology
7.1.1 Limitations
7.2 DNA Nanoball Sequencing
7.2.1 Advantages
7.2.2 Limitations
7.3 Nanopore Sequencing Technology
7.3.1 Oxford Nanopore Sequencing
7.3.2 Advantages
7.3.3 Limitations
8 Preparation of Library
9 DNA Sequencing by NGS
9.1 Whole Genome Sequencing (WGS)
9.2 Whole Exome Sequencing (WES)
9.3 Gene Panel
10 Applications of NGS
10.1 Epigenetics
10.2 Prenatal and Postnatal Diagnosis
10.3 To Detect Infectious Disease
10.4 Food and Nutrition
10.5 Cancer Research
10.6 Bioinformatics Analytics
11 Conclusion
References
Chapter 15: Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R
1 Introduction
2 Material
2.1 Computational and Software Requirements
2.2 Data Requirements
3 Methods
3.1 Loading R Libraries and Preparing Workspace
3.2 Data Preparation and Exploration
3.3 Defining Deep Learning Model Architecture
3.4 Defining Model Compilation Parameters
3.5 Training Deep Learning Model
3.6 Estimating the Accuracy of Deep Learning Model
3.7 Predicting Genome-wide Regulatory Interactions
3.8 Tuning Deep Learning for Improved Performance
4 Notes
References
Chapter 16: Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 17: Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology
1 Introduction
2 Genetic Engineering
2.1 Materials
3 Applications of Synthetic Biology
4 Methodology: Generalized Protocol for Genetic Engineering in Synthetic Biology
5 Example: Development of Integrase-mediated Differentiation Circuits to Improve Evolutionary Stability in E. coli
5.1 Introduction
5.2 Motivation
5.3 Materials and Methodology
5.4 Computational Modeling
5.5 Model Implementation and Parameters
5.5.1 Cell Growth
5.5.2 Mutations
5.5.3 Terminal Differentiation
5.5.4 Production and Burden
5.5.5 Differentiation Rates
5.6 Computational Simulations of the Model
6 Other Techniques
6.1 PCR
6.2 Gel Electrophoresis
6.3 Restriction Digestion and Ligation
7 Conclusion
References
Index