Computational Biology for Stem Cell Research

著者名：Raghav, Pawan (EDT)/Kumar PhD, Rajesh (EDT)/Lathwal PhD, Anjali (EDT)/Sharma PhD, Navneet (EDT)

Academic Press（2024/01/12発売）

• 言語：ENG
• ISBN：9780443132223
• eISBN：9780443132216

Description

Computational Biology for Stem Cell Research is an invaluable guide for researchers as they explore HSCs and MSCs in computational biology. With the growing advancement of technology in the field of biomedical sciences, computational approaches have reduced the financial and experimental burden of the experimental process. In the shortest span, it has established itself as an integral component of any biological research activity. HSC informatics (in silico) techniques such as machine learning, genome network analysis, data mining, complex genome structures, docking, system biology, mathematical modeling, programming (R, Python, Perl, etc.) help to analyze, visualize, network constructions, and protein-ligand or protein-protein interactions.This book is aimed at beginners with an exact correlation between the biomedical sciences and in silico computational methods for HSCs transplantation and translational research and provides insights into methods targeting HSCs properties like proliferation, self-renewal, differentiation, and apoptosis.- Modeling Stem Cell Behavior: Explore stem cell behavior through animal models, bridging laboratory studies to real-world clinical allogeneic HSC transplantation (HSCT) scenarios.- Bioinformatics-Driven Translational Research: Navigate a path from bench to bedside with cutting-edge bioinformatics approaches, translating computational insights into tangible advancements in stem cell research and medical applications.- Interdisciplinary Resource: Discover a single comprehensive resource catering to biomedical sciences, life sciences, and chemistry fields, offering essential insights into computational tools vital for modern research.

Table of Contents

Section I – In silico Tools and Approaches in Stem Cell Biology 1. Advancement of In Silico Tools in Stem Cell Research 2. Paradigm shift in stem cell research with computational tools, techniques, and databases 3. Stem Cell Informatics: Web-Resources Aiding in Stem Cell Research 4. Stem Cell-Based Informatics Development and Approaches 5. Application of Machine Learning-Based Approaches in Stem Cell Research 6. Stem Cell Therapy in the Era of Machine Learning 7. Computational and Stem Cell Biology: Challenges and Future Perspectives Section II – Application of Genomic and Proteomic Approaches in Stem Cell Research 8. Single Cell Transcriptome Profiling in Unravelling Distinct Molecular Signatures from Cancer Stem Cells 9. The Single-Cell Big Data Analytics: A Game-Changer in Bioscience 10. Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells 11. Cutting-Edge Proteogenomics Approaches to Analyze Stem Cells at the Therapeutic Level 12. Advances in Regenerative Medicines Based on Mesenchymal Stem Cell Secretome 13. Paradigms of Omics in Bioinformatics for Accelerating Current Trends and Future Prospects of Stem Cell Research 14. Transcriptomic Profiling-Based Identification Biomarkers of Stem Cells 15. Genomic and Transcriptomic Applications in Neural Stem Cell Therapeutics Section III – Stem Cell Network Modeling and Systems Biology 16. Integration of Multi-omic Data to Identify Transcriptional Targets During Human Hematopoietic Stem Cell Differentiation 17. Computational Approaches to Determine Stem Cell Fate 18. Stem Cell Databases and Tools: Challenges and Opportunities for Computational Biology 19. Deciphering the Complexities of Stem Cells Through Network Biology Approaches for their Application in Regenerative Medicine 20. Bioinformatics Approaches to the Understanding of Notch Signaling in the Biology of Stem Cells 21. In Silico Approaches for the Analyses of Developmental Fate of Stem Cells 22. Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A comprehensive review 23. Computational Approaches for Hematopoietic Stem Cells: Advancing Regenerative Therapeutics 24. Approaches to Construct and Analyze Stem Cells Regulatory Networks Section IV – Computational Approaches for Stem Cell Tissue Engineering 25. Tissue Engineering in Chondral Defect 26. Recent Advances in Computational Modeling: An Appraisal of Stem Cell and Tissue Engineering Research 27. Computational Approaches for Bioengineering of Cornea 28. Cheminformatics, Metabolomics and Stem Cell Tissue Engineering: A Transformative Insight 29. Targeting Cancer Stem Cells and Harnessing of Computational Tools Offer New Strategies for Cancer Therapy 30. Introduction to Machine Learning and its Applications in Stem Cell Research 31. Multiscale Computational and Machine Learning Models for Designing Stem Cell-Based Regenerative Medicine Therapies 32. Computational Analysis of Epithelial Tissue Regeneration