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.

Autorentext

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.

Inhalt

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.