This book provides a timely summary of physical modeling approaches applied to biological datasets that describe conformational properties of chromosomes in the cell nucleus. Chapters explain how to convert raw experimental data into 3D conformations, and how to use models to better understand biophysical mechanisms that control chromosome conformation. The coverage ranges from introductory chapters to modeling aspects related to polymer physics, and data-driven models for genomic domains, the entire human genome, epigenome folding, chromosome structure and dynamics, and predicting 3D genome structure.

This book provides a timely summary of physical modeling approaches applied to biological datasets that describe conformational properties of chromosomes in the cell nucleus. The coverage ranges from introductory chapters to modeling aspects related to polymer physics, and data-driven models for genomic domains, and predicting 3D genome structur

Nuclear Architecture and Dynamics provides a definitive resource for (bio)physicists and molecular and cellular biologists whose research involves an understanding of the organization of the genome and the mechanisms of its proper reading, maintenance, and replication by the cell. This book brings together the biochemical and physical characteristics of genome organization, providing a relevant framework in which to interpret the control of gene expression and cell differentiation. It includes work from a group of international experts, including biologists, physicists, mathematicians, and bioinformaticians who have come together for a comprehensive presentation of the current developments in the nuclear dynamics and architecture field. The book provides the uninitiated with an entry point to a highly dynamic, but complex issue, and the expert with an opportunity to have a fresh look at the viewpoints advocated by researchers from different disciplines. - Highlights the link between the (bio)chemistry and the (bio)physics of chromatin - Deciphers the complex interplay between numerous biochemical factors at task in the nucleus and the physical state of chromatin - Provides a collective view of the field by a large, diverse group of authors with both physics and biology backgrounds

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro.

This reference book compiles together different animal models in cancer research. It provides knowledge and a better understanding of the advancement of the molecular and cellular mechanisms associated with the progression, formation, and clinical results of various types of cancer from the evidence collected from animal models utilized for cancer research. It discusses animal models for screening anti-cancer drugs and exploration of gene therapy. It presents different methods used to construct cancer animal models and the progress of each animal model in tumor research. The book also highlights the applications of genetic engineering, including CRISP/Cas9, in designing and developing animal models for cancer research. Further, it discusses strategies for modeling animals for investigating growth, metastasis, tumor-associated inflammation and microenvironment, cancer stem cells, tumor heterogeneity, and therapeutic resistance. This book is s a valuable resource for basic and translational cancer researchers, clinicians, and health care.

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.