Computational Biology for Stem Cell Research

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. 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. 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. 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.

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