This book provides a single source of information on three major bioengineering areas: engineering at the cellular and molecular level; biomedical devices / instrument engineering; and data engineering. It explores the latest strategies that are essential to advancing our understanding of the mechanisms of human diseases, the development of new enzyme-based technologies, diagnostics, prosthetics, high-performance computing platforms for managing huge amounts of biological data, and the use of deep learning methods to create predictive models. The book also highlights the growing importance of integrating chemistry into life sciences research, most notably concerning the development and evaluation of nanomaterials and nanoparticles and their interactions with biological material. The underlying interdisciplinary theme of bioengineering is addressed in a range of multifaceted applications and worked out examples provided in each chapter.
Dr. Renu Vyas is Head of the MIT School of Bioengineering Sciences & Research, a constituent unit of MIT-ADT University Pune. She received her Ph.D. from the CSIR-NCL, Pune, India and subsequently carried out post-doctoral research at the University of Tennessee, Knoxville, USA. She has a multidisciplinary background and has held advanced positions in academia, research, and industry. She has published more than 30 international research papers, holds 15 patents, and co-authored a book on practical chemoinformatics with Springer. She is an associate editor for the journal Novel Approaches in Drug Designing and Development (NAPDD) and serves on the editorial board of the Journal of Integrated Technologies. Her research interests include drug design, machine learning, NGS, biosensors and big data analytics. She is the recipient of numerous national and international fellowships, projects and travel grants. Throughout her teaching career, she has taught various interdisciplinary subjects such as advanced chemoinformatics, systems biology, algorithms in bioinformatics, molecular modeling and drug design, proteomics , machine learning and artificial intelligence.