Mandal Reverse Engineering of Regulatory Networks

1. Auflage 2024
ISBN: 978-1-0716-3461-5
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2719, 327 Seiten, eBook

Reihe: Methods in Molecular Biology

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. 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Mandal, Sudip

Inhaltsverzeichnis

1. Molecular Modeling Techniques and in-Silico Drug Discovery

Angshuman Bagchi

2. Systems Biology Approach to Analyse Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous cell Carcinoma

Jyotsna Choubey, Olaf Wolkenhauer, and Tanushree Chatterjee

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

Sagar Bag and Sudipta Bhowmik

4. Inference of Dynamic Growth Regulatory Network in Cancer Using high-Throughput Transcriptomic Data

Aparna Chaturvedi and Anup Som

5. Implementation of Exome Sequencing to Identify Rare Genetic Diseases

Prajna Udupa and Debasish Kumar Ghosh

6. 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, SonuKurmi, and Jyoti Kant Choudhari

7. New Insights into Clinical Management for Sickle-Cell Disease: Uncovering the Significance Pathways Affected By the Involvement of Sickle Cell Disease

Usha Chouhan, Trilok janghel, Shaifali bhatt , Sonu Kurmi, and Jyoti Kant Choudhari

8. A Review on Computational Approach for S-system Based Modeling of Gene Regulatory Network

Sudip Mandal and Pijush Dutta

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

Aanchal Gupta, Shubham Kumar, and Ashwani Kumar

10. Identification of Culprit Genes for Different Diseases by Analysing Microarray Data

Ayushman Kumar Banerjee, Shrayana Ghosh, and Chittabrata Mal

11. Big Data Analysis in Computational Biology and Bioinformatics

Prakash Kumar, Ranjit Kumar Paul, Himadri Shekhar Roy, Md. Yeasin, Ajit,  and Amrit Kumar Paul

12. Prediction and Analysis of Transcription Factor Binding Sites to Understand Gene Regulation: Practical Examples and Case Studies using R Programming

Vijaykumar Yogesh Muley

13. Hubs and Bottlenecks in Protein-Protein Interaction Networks

Chandramohan Nithya, Manjari Kiran, and Hampapathalu Adimurthy Nagarajaram

14. Next-Generation Sequencing to Study the DNA Interaction

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

Vijaykumar Yogesh Muley,hammai Kathiresan, Srinithi Ramachandran, and Langeswaran Kulathaivel

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

Vijaykumar Yogesh Muley

16. Computational inference of Gene Regulatory Network using genome-wide ChIP-X data

Samayaditya Singh, Manjari Kiran, and Pramod R. Somvanshi

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

Gopikrishnan Bijukumar and Pramod R. Somvanshi