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Elsevier

Computational Biology for Stem Cell Research

  • 1st Edition - January 12, 2024
  • Latest edition
  • Editors: Pawan Raghav, Rajesh Kumar, Anjali Lathwal, Navneet Sharma
  • Language: English

Computational Biology for Stem Cell Research seamlessly bridges the gap between the worlds of biomedical sciences and in silico computational methods. This book serves as a… Read more

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Description

Computational Biology for Stem Cell Research seamlessly bridges the gap between the worlds of biomedical sciences and in silico computational methods. This book serves as a valuable resource for researchers and students, enabling them to grasp and delve into the intricacies of hematopoietic Stem Cells (HSCs) and mesenchymal Stem Cells (MSCs) through the lens of computational biology. This perspective sheds light on stem cell transplantation, translational research, and unique properties of stem cells like self-renewal and differentiation. In addition to introducing readers to stem cell-focused bioinformatics tools, this resource offers a clear pathway for effortlessly merging in silico methods with traditional in vitro and in vivo approaches.

Computational Biology for Stem Cell Research combines science and technology to showcase how computational methods transform stem cell research by reducing costs and enhancing investigations. The chapters uncover various approaches, from machine learning to genome analysis, for studying networks, protein interactions, dynamics, and the preprocessing of large datasets. The book aims to give readers a broad view of the advanced computational tools and methods extensively employed in stem cell research. Additionally, the book emphasizes the ongoing studies and tools yet to be developed for furthering stem cell research.

Key features

  • 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.

Readership

Researchers, Industry partners, post-graduate students in stem cells, regenerative medicine and those involved in computational biology and bioinformatics

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

Product details

  • Edition: 1
  • Latest edition
  • Published: January 12, 2024
  • Language: English

About the editors

PR

Pawan Raghav

Dr. Pawan Kumar Raghav completed his MSc in Bioinformatics (2008) from Punjabi University Patiala, India; PG Diploma in Chemoinformatics (2009) from Jamia Hamdard; MPhil in Bioinformatics (2010) from The Global Open University, Nagaland; and PhD at the Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi in Life Sciences from Bharathiar University, Coimbatore. During his Ph.D., he had designed molecules and evaluated their applications through response modification that regulates stem cells proliferation, differentiation, and apoptosis. His recent research activities are in the field of machine learning, deep learning, and molecular biology, as well as in the development of new scoring function parameterizations for use in docking, simulations, and complex network analysis. Currently, he is working as Scientist ‘D’ in Stem Cell Facility, AIIMS on stem cell informatics to establish a bridge between the experimental science and computational biology.
Affiliations and expertise
BioExIn, Delhi, India

RK

Rajesh Kumar

Dr. Rajesh Kumar Currently he is a postdoctoral fellow at the Developmental Therapeutics Branch of National Cancer Institutes, NIH, USA. His research interest includes immunotherapeutic for cancer and other infectious diseases; analysis of multi-omics data for biomarker identification; understanding genomic structural alterations in cancers; peptide-based therapeutics, developing algorithms for clinical data analysis and interpretation using machine learning for precision medicine. He has published over 25 publications, including international research articles and book chapters. He is a member of the Asia-Pacific Bioinformatics Network society. He was the recipient of CSIR- travel award for delivering the presentation at Gordon research Conference on Genomic Instability and Cancer at Ventura, California USA in 2020. He was also recipient of Carl Storm International Fellowship award in 2020.
Affiliations and expertise
BioExIn, Delhi, India

AL

Anjali Lathwal

Dr. Anjali Lathwal is a researcher in the department of Computational Biology at Indraprastha Institute of Information Technology, Delhi. Her areas of expertise include Applied Machine Learning; Monte-Carlo Simulations; Mathematical Modeling; Computer-aided Vaccine Design; Peptide-based Therapeutics; Cancer Immunotherapy; Biomarkers Discovery; Genomic Annotation & Instability; Survival Modeling; Drug Repurposing; Network Modelling; Database Designing, Management, and Integration. She has published over 10 peer-reviewed publications in renowned international journals, book chapters as a co-author, and done several independent consultancy projects. She is also a member of the Asia-Pacific Bioinformatics Network society and recipient of the Research Excellence Award from the Institute of Scholars. Her research experience shows her substantial contribution to the field of viral therapeutics, biomarker discovery, and computational biology.
Affiliations and expertise
Researcher, Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT), New Delhi, India

NS

Navneet Sharma

Dr. Navneet Sharma is an Assistant Professor at the Amity Institute of Pharmacy, Amity University, India. He has an M.Pharm, Ph.D and PGDRA, and his expertise lies in the realm of biomaterials and applied R & D, especially needs-based product development. He has taken pivotal roles as an investigator in three projects supported by DST-India. He has won several awards, including eight national and international awards. The most prestigious among them are SCO and Ministry of External Affairs, Government of India Covid-19 best innovation award 2020, and Department of Science and Technology, Young Scientist Award for the year 2018 and 2022. He has more than 40 publications including four books, five book chapters, 10 patents and 4 technologies successfully transferred to the industry.

Affiliations and expertise
Scientist, Institute of Information Technology (IIT), Delhi, India

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