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Reverse Engineering of Regulatory Networks

  • Book
  • © 2024

Overview

Editors:
  1. Sudip Mandal

    1. Department of Electronics and Communication Engineering, Jalpaiguri Govt. Engineering College, Jalpaiguri, India

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  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2719)

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About this book

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. 

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Table of contents (17 protocols)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Molecular Modeling Techniques and In-Silico Drug Discovery

    • Angshuman Bagchi
    Pages 1-11

  3. Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma

    • Jyotsna Choubey, Olaf Wolkenhauer, Tanushree Chatterjee
    Pages 13-31

  4. Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures

    • Sagar Bag, Sudipta Bhowmik
    Pages 33-49

  5. Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

    • Aparna Chaturvedi, Anup Som
    Pages 51-77

  6. Implementation of Exome Sequencing to Identify Rare Genetic Diseases

    • Prajna Udupa, Debasish Kumar Ghosh
    Pages 79-98

  7. Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures

    • Usha Chouhan, Rakesh Kumar Sahu, Shaifali Bhatt, Sonu Kurmi, Jyoti Kant Choudhari
    Pages 99-119

  8. New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease

    • Usha Chouhan, Trilok Janghel, Shaifali Bhatt, Sonu Kurmi, Jyoti Kant Choudhari
    Pages 121-132

  9. A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network

    • Sudip Mandal, Pijush Dutta
    Pages 133-152

  10. Big Data in Bioinformatics and Computational Biology: Basic Insights

    • Aanchal Gupta, Shubham Kumar, Ashwani Kumar
    Pages 153-166

  11. Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data

    • Ayushman Kumar Banerjee, Shrayana Ghosh, Chittabrata Mal
    Pages 167-180

  12. Big Data Analysis in Computational Biology and Bioinformatics

    • Prakash Kumar, Ranjit Kumar Paul, Himadri Shekhar Roy, Md. Yeasin, Ajit, Amrit Kumar Paul
    Pages 181-197

  13. Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming

    • Vijaykumar Yogesh Muley
    Pages 199-225

  14. Hubs and Bottlenecks in Protein-Protein Interaction Networks

    • Chandramohan Nithya, Manjari Kiran, Hampapathalu Adimurthy Nagarajaram
    Pages 227-248

  15. Next-Generation Sequencing to Study the DNA Interaction

    • Nachammai Kathiresan, Srinithi Ramachandran, Langeswaran Kulanthaivel
    Pages 249-264

  16. Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R

    • Vijaykumar Yogesh Muley
    Pages 265-294

  17. Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data

    • Samayaditya Singh, Manjari Kiran, Pramod R. Somvanshi
    Pages 295-306

  18. Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology

    • Gopikrishnan Bijukumar, Pramod R. Somvanshi
    Pages 307-324

  19. Back Matter

    Pages 325-327
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Editors and Affiliations

  • Department of Electronics and Communication Engineering, Jalpaiguri Govt. Engineering College, Jalpaiguri, India

    Sudip Mandal

Bibliographic Information

  • Book Title: Reverse Engineering of Regulatory Networks

  • Editors: Sudip Mandal

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-3461-5

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

  • Hardcover ISBN: 978-1-0716-3460-8Published: 08 October 2023

  • Softcover ISBN: 978-1-0716-3463-9Due: 21 October 2024

  • eBook ISBN: 978-1-0716-3461-5Published: 06 October 2023

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 327

  • Number of Illustrations: 8 b/w illustrations, 64 illustrations in colour

  • Topics: Biomedical Engineering and Bioengineering, Genetics and Genomics, Bioinformatics

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