The biological sciences have been among the most exciting and intensely pursued fields of science for the past several decades. The advancement of high-throughput technologies that generate large scale biological data as well as the development of related computational tools has enabled global efforts at understanding complex biological systems and brought revolutionary changes to biological research. Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications summarizes some of the most recent research carried out in computational biology and systems biology to encourage and guide future study. Submissions to this comprehensive text present methods, tools, and applications developed and considered by many leading experts around the globe.

Interdisciplinary Research and Applications in Bioinformatics, Computational Biology, and Environmental Sciences is a collection of cutting-edge research papers in the field of computational and systems biology contributed by leading researchers from across the globe. The studies range from the atomic/molecular level to the genomic level and present a wide spectrum of important biological problems and applications. It is a must-read for researchers in a broad range of disciplines, including computer science, chemistry, physics, math and statistics, and biological sciences.

This text examines in detail mathematical and physical modeling, computational methods and systems for obtaining and analyzing biological structures, using pioneering research cases as examples. As such, it emphasizes programming and problem-solving skills. It provides information on structure bioinformatics at various levels, with individual chapters covering introductory to advanced aspects, from fundamental methods and guidelines on acquiring and analyzing genomics and proteomics sequences, the structures of protein, DNA and RNA, to the basics of physical simulations and methods for conformation searches. This book will be of immense value to researchers and students in the fields of bioinformatics, computational biology and chemistry. Dr. Dongqing Wei is a Professor at the Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China. His research interest is in the general area of structural bioinformatics.

The cost of drug development is increasing, and investment returns are decreasing. The number of drugs approved by FDA is in decline in terms of the number of new molecular entities (NMEs). Amongst the reasons noted for this are the adverse side effects and reduced efficiency of many of the potential compounds. This is a problem both for the pharmaceutical industry and for those suffering from diseases for which there are no or few available treatments. Advances in computational chemistry, computer science, structural biology and molecular biology have all contributed to improved drug design strategies and reduced the time taken for drug discovery. By interfacing cheminformatics and bioinformatics with systems biology we can create a powerful tool for understanding the mechanisms of patho-physiological systems and identifying lead molecules for various diseases. This integration of drug design approaches can also play a crucial role in the prediction and rationalization of drug effects and side effects, improving safety and efficacy and leading to better approval rates. Addressing the lack of knowledge on the fundamental aspects of the various computational tools for drug discovery, this book is a compilation of recent bioinformatics and cheminformatics approaches, and their integration with systems biology. Written primarily for researchers and academics in chem- and bioinformatics, it may also be a useful resource for advanced-level students.

Over the past decade, great strides have been taken in developing methodologies that can treat more and more complex nano- and nano-bio systems embedded in complex environments. Multiscale Dynamics Simulations covers methods including DFT/MM-MD, DFTB and semi-empirical QM/MM-MD, DFT/MMPOL as well as Machine-learning approaches to all of the above. Focusing on key methodological breakthroughs in the field, this book provides newcomers with a comprehensive menu of multiscale modelling options so that they can better chart their course in the nano/bio world.