Systems Biomedicine Approaches in Cancer Research

1. Auflage 2022
ISBN: 978-981-19-1955-8
Verlag: Springer

Buch, Englisch, 163 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g

This book presents the applications of systems biology and synthetic biology in cancer medicine. It highlights the use of computational and mathematical models to decipher the complexity of cancer heterogeneity. The book emphasizes the modeling approaches for predicting behavior of cancer cells, tissues in context of drug response, and angiogenesis. It introduces cell-based therapies for the treatment of various cancers and reviews the role of neural networks for drug response prediction. Further, it examines the system biology approaches for the identification of medicinal plants in cancer drug discovery. It explores the opportunities for metabolic engineering in the realm of cancer research towards development of new cancer therapies based on metabolically derived targets. Lastly, it discusses the applications of data mining techniques in cancer research. This book is an excellent guide for oncologists and researchers who are involved in the latest cancer research.

Zielgruppe

Research

Autoren/Hrsg.

Singh, Shailza

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

Chapter 1 _Systems Complexity in Cancer.- Chapter 2_ Engineered Biotherapeutics through Synthetic Biology in Cancer.- Chapter 3_ Cancer Immunotherapy: A Potential Convergence between Systems and Synthetic Biology.- Chapter 4_ Cell Based Therapeutic Devices in Cancer.- Chapter 5_ Case Studies on Medicinal Plants in Cancer Drug Discovery using System Approaches.- Chapter 7 _Metabolic engineering and synthetic biology devices in treating Cancer.-  Chapter 8 _Cancer Biomarkers in the era of Systems Biology.- Chapter 9_ Supervised vs Non-Supervised Learning to Combat Cancer.- Chapter 10 _Designing Cancer Biological Systems using Synthetic Engineering.- Chapter 11_ Biosystems and Genetic Engineering Tools in Cancer Theranostics.- Chapter 12_ Role of HPC in Cancer Informatics.- Chapter 13_ Statistical ML for Cancer Therapeutics.- Chapter 14 _Data Mining and Knowledge Discovery in Cancer.- Chapter 15 _TCGA Data from TensorFlow Optimization.

Über Autor(innen)

Dr. Shailza Singh  is serving as Scientist E and in charge of the bioinformatics and high performance computing facility. Her lab focuses on systems and synthetic biology of infectious disease and cancer model systems, wherein she is trying to integrate the action of regulatory circuits, cross talk between pathways, and non-linear kinetics of biochemical processes through mathematical modeling. She is the recipient of several awards such as RGYI, DST-Young Scientist, INSA Bilateral Exchange, and SAKURA Exchange Programme. Dr. Singh is also serving as a reviewer and academic editor of various international journals of repute.