Computational Biology for Stem Cell Research

1st Edition - January 12, 2024
Imprint: Academic Press
Editors: Pawan Raghav, Rajesh Kumar, Anjali Lathwal, Navneet Sharma
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 2 2 2 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 2 2 1 - 6

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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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
Abbreviations
List of figures
List of tables
Contributors
Biographies
Preface
Acknowledgments
Introduction
Section I. In silico tools and approaches in stem cell biology
Chapter 1. Advancement of in silico tools for stem cell research
1. Introduction
2. In silico tools and approaches
3. Conclusion and future prospects
Chapter 2. Paradigm shift in stem cell research with computational tools, techniques, and databases
1. Introduction
2. Big data in stem cell research
3. Computational methods for effective analyses of high-throughput stem cells data
4. Computational resources for stem cell data analysis
5. Cancer stem cells
6. Conclusion
Chapter 3. Stem cell informatics: Web resources aiding in stem cell research
1. Introduction
2. Discussion
3. Conclusion
Chapter 4. Stem cell based informatics development and approaches
1. Introduction
2. Applications of stem cells: a promise to behold
3. Expanding horizons of omics and advent of informatics
4. Network biology and stem cell informatics
5. Recent developments in stem cell informatics
6. Future of the stem cell informatics
Chapter 5. Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A comprehensive review
1. Introduction
2. Ideal requisite for molecular imaging
3. Imaging strategies employed in regenerative medicine
4. Imaging methods using nuclear medicine
5. Software programs used in stem cell research
6. Current challenges in regenerative medicine imaging
7. Artificial intelligence in stem cell imaging
8. Future of stem cell imaging with artificial intelligence
9. Conclusion
Chapter 6. Application of machine learning–based approaches in stem cell research
1. Introduction
2. Types of stem cells and their therapeutic applications
3. Overview of machine learning–based model building
4. Applications of machine learning in stem cell–based therapies
5. Applications of deep learning in stem cell–based therapies
6. Evolution of nature-inspired computing and its applications in stem cell–based therapies
7. Conclusion and future aspects
Chapter 7. Stem cell therapy in the era of machine learning
1. Introduction
2. Current state of stem cell therapies
3. Machine learning applications in different biological and medicine fields
4. Current trends in machine learning–based stem cell therapy
5. Conclusion and future prospects
Chapter 8. Computational and stem cell biology: Challenges and future perspectives
1. Introduction
2. Current challenges and contests: Crossing hurdles in stem cell and computational biology
3. Disease modeling and computational biology: Current challenges
4. Differentiating stem cells, desired cell types, and organoids: Challenges and future aspects
5. Future prospective and outlook
6. Conclusion
Section II. Application of genomic and proteomic approaches in stem cell research
Chapter 9. Single-cell transcriptome profiling in unraveling distinct molecular signatures from cancer stem cells
1. Introduction
2. Cancer stem cells
3. Single-cell transcriptomics
4. Single-cell data analysis workflow
5. Single-cell studies on detecting distinct molecular signature in cancer
6. Future perspectives
Chapter 10. The single-cell big data analytics: A game changer in bioscience
1. Introduction
2. Techniques for producing single-cell big data in revolutionizing regenerative medicine healthcare
3. Unlocking the potential of stem cells through single-cell data analysis
4. The promising future of single-cell data in advancing stem cell biology research
5. Conclusion
Chapter 11. Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells
1. Introduction
2. Genomic and proteomic aspects to understand stem cell phenotype
3. Mechanistic insights into the stemness phenotype of stem cells through genomic and proteomic platform
4. Conclusion
Chapter 12. Cutting-edge proteogenomics approaches to analyze stem cells at the therapeutic level
1. Stem cell characterization
2. Conclusion
Chapter 13. Cheminformatics, metabolomics, and stem cell tissue engineering: A transformative insight
1. Introduction
2. Computational tools and stem cell biology
3. Computational approaches, genomics and proteomics
4. Cheminformatics, tissue engineering, and stem cell research
5. Metabolomics data acquisition and preprocessing
6. Manipulation of stem cells and tissue regeneration
7. Stem cell microenvironment and cardiac tissue regeneration: engineered approaches
8. Future prospective and present outlook
9. Conclusion
Chapter 14. Advances in regenerative medicines based on mesenchymal stem cell secretome
1. Introduction
2. MSC-Secretome: conditioned media from mesenchymal stem cells and exosomes
3. Effector biological functions of MSC-Secretome and their applications
4. Clinical studies with secretome from mesenchymal stem cells
5. Applications of computational tools in stem cell–based therapies
6. Conclusion
Chapter 15. Paradigms of omics in bioinformatics for accelerating current trends and prospects of stem cell research
1. Introduction
2. A brief overview of current therapeutic uses of stem cell treatment
3. Characterization of stem cells using OMICS
4. Transcriptomic and proteomic methods in stem cell characterization
5. Multiomics collaboration
6. Potency, omics approaches, and knowledge of the mechanism of action
7. Importance of computational modeling in stem cell research
8. Research on stem cells: applications of several modeling types
9. Bioinformatics in stem cell research
10. Stem cell research mechanistic modeling frameworks
11. Types of modeling in stem cell research
12. Computational modeling applications in regenerative medicine
13. Omics-based methods for managing the manufacturing of biomaterials
14. Challenges in computational analysis in stem cell research
15. Conclusion
Chapter 16. Transcriptomic profiling–based identification of biomarkers of stem cells
1. Introduction
2. Timeline of main milestones in technologies for gene expression
3. Working methodology
4. Various platforms and strategies in RNA-Seq
5. Conclusion and future perspectives
Chapter 17. Genomic and transcriptomic applications in neural stem cell therapeutics
1. Introduction
2. Genomic approaches to study stem cells
3. Genomic methods in therapeutic application of neural stem cells
4. Transcriptomics
5. Bottlenecks in -omics application in stem cell model for neurological disorders
Section III. Stem cell network modeling and systems biology
Chapter 18. Integration of multi-omic data to identify transcriptional targets during human hematopoietic stem cell erythroid differentiation
1. Introduction
2. Rationale
3. Experimental approach
4. Bioinformatic pipelines for analyzing transcriptional regulation
5. Uncovering GATA-bound regulatory regions: GATA1 and GATA2 ChIP-seq data integration reveals stage-specific TF switching during erythroid differentiation
6. Conclusion and future directions—a therapeutic approach
Chapter 19. Computational approaches to determine stem cell fate
1. Introduction
2. Understanding stem cell fates during differentiation: new technologies—software and models
3. Rise of gene regulatory networks: nodes, motifs, gene batteries, and mathematical models
4. Conclusion: future of stem cell engineering applications
Chapter 20. Stem cell databases and tools: challenges and opportunities of computational biology
1. Introduction
2. Applications of computational biology for stem cells research
3. Stem cell databases
4. Cancer stem cell databases
5. Resources
6. Stem cell tools
7. Conclusion and future prospects
Chapter 21. Deciphering the complexities of stem cells through network biology approaches for their application in regenerative medicine
1. Introduction
2. Key genes and pathways regulating pluripotent stem cell fate
3. Bioinformatics in stem cell research
Case study: Network analysis using Cytoscape for human pluripotent stem cells
Chapter 22. Bioinformatics approaches to the understanding of Notch signaling in the biology of stem cells
1. Introduction
2. Notch signaling
3. Applications of bioinformatics in signaling pathways
4. Bioinformatics approach in the understanding of Notch signaling in the biology of stem cells
5. Clinical studies
6. Conclusion
Chapter 23. In silico approaches for the analysis of developmental fate of stem cells
1. Introduction
2. Computational techniques in stem and development biology
3. Stem cell biology with OMICs
4. Epigenetic memory and induced pluripotent stem cells
5. Stem cell biology through computation
6. Single cell genomics with spatial resolution
7. Mathematical modeling
8. Weighted correlation network analysis
9. Gene set enrichment analysis
10. Machine learning classifiers
11. Conclusion
Chapter 24. Computational approaches for hematopoietic stem cells: Advancing regenerative therapeutics
1. Introduction
2. Big data analysis of HSCs
3. Metabolic cross-talk
4. Computational approaches for stem cell research
5. Computational biology in HSCT
6. Major concerns and drawbacks
7. Conclusion and future perspectives
Chapter 25. Approaches to construct and analyze stem cells regulatory networks
1. Introduction
2. Machine learning methods for stem cell regulatory network prediction
3. Toward multiomics data integration for stem cell networks
4. Constructing the stem cell gene coexpression networks
5. Constructing the stem cell regulatory networks
6. Studies using integrated bioinformatics and network-based approaches in stem cells research
7. Conclusion and future perspectives
Section IV. Computational approaches for stem cell tissue engineering
Chapter 26. Tissue engineering in chondral defect
1. Introduction
2. Cartilage structure and signaling
3. Signaling in tissue engineering
4. Pathophysiology of chondral defects and degeneration
5. Management of chondral injuries
6. Diagnosis of chondral defects
7. Management of chondral defects
8. Molecular methods of chondral defects management
9. Computational tissue engineering
10. Limitations
11. Conclusions
Chapter 27. Recent advances in computational modeling: An appraisal of stem cell and tissue engineering research
1. Introduction
2. The node of computational stem cell biology
3. A brief introduction to common modeling approaches
4. Types of stem cell-based modeling
5. Single-cell isolation-related tools and software
6. Multiscale computational modeling: from intracellular networks to cell-cell interactions in the tissue
7. Frameworks for mechanistic modeling in stem cell research: an overview
8. Computational modeling associated with stem cells in the era of single-cell big data production
9. Current obstacles and future possibilities
10. SWOT analysis of computational approaches
11. Overview of cell modeling: from basic 2D to 3D innovative system
12. Computer-aided tissue engineering and regenerative medicines
13. Bio-CAD
14. Biomimetic designing
15. Constriction of stem cell-based tissue-engineered patient-specific implants
16. Application of computational modeling to regenerative medicine
17. Concluding remarks
18. Future prospects
Chapter 28. Computational approaches for bioengineering of cornea
1. Introduction
2. Ultrastructure of the cornea
3. Corneal diseases
4. Corneal transplantation
5. Biomaterials for corneal bioengineering
6. Computer-aided tissue engineering
7. Conclusion
8. Future prospective
Chapter 29. Targeting cancer stem cells and harnessing of computational tools offer new strategies for cancer therapy
1. Introduction
2. Targeting cancer stem cells for therapy
3. Reactive oxygen species and sources
4. Reactive oxygen species level in cancer stem cells and drug resistance
5. Cancer stem cell for secretion and delivery of anticancer drugs
6. Issues of secondary cancer
7. In silico modeling of drug development for cancer treatment
8. In silico trial methods for anticancer drugs
9. Cancer driver genes and computational methods
10. Developing new cancer therapies
11. Prospects for anticancer drug screening
12. Conclusion
Chapter 30. Introduction to machine learning and its applications in stem cell research
1. Introduction
2. Introduction to machine learning
3. Applications of deep learning in stem cell research
4. Discussion
5. Challenges and future directions
Chapter 31. Multiscale computational and machine learning models for designing stem cell-based regenerative medicine therapies
1. Introduction
2. Attributes of multiscale computational modeling for stem cell research
3. Bioinformatics approach in stem cell
4. Artificial intelligence based supervised and unsupervised modeling approach
5. Mechanistic modeling approach
6. Dynamical modeling approach
7. Spatiotemporal analysis
8. Comparing approaches & future perspectives of computational models for stem cell-based regenerative medicine therapies
9. Stem cell population modeling in wound healing
10. Insights into stem cell therapy for vision defects
11. Regeneration of pacemakers using stem cells
12. Interpretation of scRNA-seq for the usage of MSCs in regenerative engineering
13. Conclusion
Chapter 32. Computational analysis in epithelial tissue regeneration
1. Introduction
2. Epithelial tissue
3. Physiology of epithelial tissue regeneration
4. Computational biology and bioinformatics
5. Computational analysis in epithelial tissue regeneration
6. Examples of computational models and tools in epithelial tissue regeneration
7. Conclusion
Author index
Index

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

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

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

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

Life Sciences

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Computational Biology for Stem Cell Research

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Navneet Sharma

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