Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

A range of theories on the rates of evolution-from static to gradual to punctuated to quantum-have been developed, mostly by comparing morphological changes over geological timescales as described in the fossil record.

This volume focuses on the use of system genetic methods and the use of murine models to study the role of gene variants and environmental factors on human health and disease--what is now often called personalized or precision health care. The protocols in this book will help readers analyze genetic causes of heritable variation across a wide range of systems and traits using rodent models. The chapters in this book are separated into three sections that cover: 1) resources for systems genetics; 2) tools for analysis and integration in systems genetics; and 3) systems genetics use cases. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and tools, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Practical and thorough, Systems Genetics: Methods and Protocols is a valuable resource for anyone who is interested in this diverse field.

This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.

Defines the current status of research in the genetics, anatomy, and development of the nematode C. elegans, providing a detailed molecular explanation of how development is regulated and how the nervous system specifies varied aspects of behavior. Contains sections on the genome, development, neural networks and behavior, and life history and evolution. Appendices offer genetic nomenclature, a list of laboratory strain and allele designations, skeleton genetic maps, a list of characterized genes, a table of neurotransmitter assignments for specific neurons, and information on codon usage. Includes bandw photos. For researchers in worm studies, as well as the wider community of researchers in cell and molecular biology. Annotation copyrighted by Book News, Inc., Portland, OR

Technological advances over the last two decades have placed genetic research at the forefront of sport and exercise science. It provides potential answers to some of contemporary sport and exercise’s defining issues and throws up some of the area’s most challenging ethical questions, but to date, it has rested on a fragmented and disparate literature base. The Routledge Handbook of Sport and Exercise Systems Genetics constitutes the most authoritative and comprehensive reference in this critical area of study, consolidating knowledge and providing a framework for interpreting future research findings. Taking an approach which covers single gene variations, through genomics, epigenetics, and proteomics, to environmental and dietary influences on genetic mechanisms, the book is divided into seven sections. It examines state-of-the-art genetic methods, applies its approach to physical activity, exercise endurance, muscle strength, and sports performance, and discusses the ethical considerations associated with genetic research in sport and exercise. Made up of contributions from some of the world’s leading sport and exercise scientists and including chapters on important topical issues such as gene doping, gender testing, predicting sport performance and injury risk, and using genetic information to inform physical activity and health debates, the handbook is a vital addition to the sport and exercise literature. It is an important reference for any upper-level student, researcher, or practitioner working in the genetics of sport and exercise or exercise physiology, and crucial reading for any social scientist interested in the ethics of sport.