Reverse Engineering of Regulatory Networks / edited by Sudip Mandal
Molecular Modeling Techniques and in-Silico Drug Discovery -- Systems Biology Approach to Analyse Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous cell Carcinoma -- Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures -- Inference of Dynamic Growth Regulatory Network in Cancer Using high-Throughput Transcriptomic Data -- Implementation of Exome Sequencing to Identify Rare Genetic Diseases -- Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures -- New Insights into Clinical Management for Sickle-Cell Disease: Uncovering the Significance Pathways Affected By the Involvement of Sickle Cell Disease -- A Review on Computational Approach for S-system Based Modeling of Gene Regulatory Network -- Big Data in Bioinformatics and Computational Biology: Basic Insights -- Identification of Culprit Genes for Different Diseases by Analysing Microarray Data -- Big Data Analysis in Computational Biology and Bioinformatics -- Prediction and Analysis of Transcription Factor Binding Sites to Understand Gene Regulation: Practical Examples and Case Studies using R Programming -- Hubs and Bottlenecks in Protein-Protein Interaction Networks -- Next-Generation Sequencing to Study the DNA Interaction Nac Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R -- Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R -- Computational inference of Gene Regulatory Network using genome-wide ChIP-X data -- Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic 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. ..
| Medienart: |
E-Book |
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| Erscheinungsjahr: |
2024. |
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| Erschienen: |
New York, NY: Springer US ; 2024. New York, NY: Imprint: Humana ; 2024. |
| Ausgabe: |
1st ed. 2024. |
| Reihe: |
Methods in Molecular Biology - 2719 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mandal, Sudip [HerausgeberIn] |
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| Links: |
doi.org [lizenzpflichtig] |
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| ISBN: |
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| Themen: |
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| Umfang: |
1 Online-Ressource(X, 327 p. 72 illus., 64 illus. in color.) |
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| doi: |
10.1007/978-1-0716-3461-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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1861140851 |
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| 520 | |a 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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