Learning Basic Genetics With Interactive Computer Programs

Traditionally, genetics laboratory exercises at the university level focus on mono- and dihybrid crosses and phenotypic analysis—exercises under traditional time, materials, and process constraints. Lately, molecular techniques such as gene cloning, polymerase chain reactions (PCR), and bioinformatics are being included in many teaching laboratories—where affordable. Human chromosome analysis, when present at all, has often been restricted to simple identification of chromosomes by number, through the usual “cut-and-paste” method. Although several online karyotyping (chromosome identification) programs have become available, they are not meaningful for studying the dynamics of the chromosome system, nor do they help students understand genetics as a discipline. The software that accompanies this book has been shown to be an ideal tool for learning about genetics, which requires a combination of understanding, conceptualization, and practical experience.

Crossover is a laboratory manual and computer program that work together to teach the principles of genetics. Designed to complement regular textbooks and classroom instruction, Crossover consists of thirty-five modules that can be tailored to fit genetics courses at several levels. Examples, interactive computer models, problems, and self-tests all help students understand difficult concepts and learn the basic mathematical skills needed to study contemporary theories of genetics, evolution, and breeding. The easy-to-use tutorial system lets students work at their own pace. Features include: - In-depth investigations of meiosis, genetic ratios, linkage mutation, natural selection, Hardy-Weinberg equilibrium, artificial selection, quantitative genetics, breeding methods, mating designs, plant patent law, and the use of molecular markers - A computer model that allows students to manipulate genetic parameters and compare outcomes. Students can observe evolution and artificial selection in action - A "Major Concepts" section at the beginning of each chapter to help students focus on the important material to be learned - A visual, easy-to-understand presentation of material - Exercises based on genetic data and analyses from actual research projects - Several stages of complexity within each area of instruction. - Instant grading of exercises - "Suggested Readings" at the end of each chapter to direct the student to related books, articles, and computer programs.

To order this title for shipment to Austria, Germany, or Switzerland, please contact dpunkt verlag directly. "[The authors] have performed a remarkable double service with this excellent book on genetic programming. First, they give an up-to-date view of the rapidly growing field of automatic creation of computer programs by means of evolution and, second, they bring together their own innovative and formidable work on evolution of assembly language machine code and linear genomes." --John R. Koza Since the early 1990s, genetic programming (GP)-a discipline whose goal is to enable the automatic generation of computer programs-has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

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.

This unique introduction to GP provides a detailed overview of the subject and its antecedents with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

This new publication in the Models and Modeling in Science Education series synthesizes a wealth of international research on using multiple representations in biology education and aims for a coherent framework in using them to improve higher-order learning. Addressing a major gap in the literature, the volume proposes a theoretical model for advancing biology educators’ notions of how multiple external representations (MERs) such as analogies, metaphors and visualizations can best be harnessed for improving teaching and learning in biology at all pedagogical levels. The content tackles the conceptual and linguistic difficulties of learning biology at each level—macro, micro, sub-micro, and symbolic, illustrating how MERs can be used in teaching across these levels and in various combinations, as well as in differing contexts and topic areas. The strategies outlined will help students’ reasoning and problem-solving skills, enhance their ability to construct mental models and internal representations, and, ultimately, will assist in increasing public understanding of biology-related issues, a key goal in today’s world of pressing concerns over societal problems about food, environment, energy, and health. The book concludes by highlighting important aspects of research in biological education in the post-genomic, information age.

Cancer genetics is a quickly growing field within oncology. The ability to identify individuals at high risk for cancer improves the chance of early prevention and detection of cancer. The results of genetic testing affect quality of life, employment, and ability to be insured. This volume will provide an overview of cancer genetics, inherited cancer susceptibility, and available services and testing, including both the risks and benefits of testing. Some of the topics covered include: genetics of breast and ovarian cancer, testing minors for inherited cancer risk, chemoprevention of heritable cancer risk, genetics of colorectal cancer, insurance issues in genetic testing for cancer, ethical and legal issues in genetic testing for cancer susceptibility, testing for breast cancer risk in the Ashkenazim, estimating individualized risk of breast cancer, genetic counseling for the individual with inherited cancer susceptibility, and components of a genetic cancer risk clinic. While heritable cancers account for between five and ten percent of all cancer cases, molecular alterations attributable to specific inherited cancer susceptibility may give us important clues into the mechanism by which cancer occurs, not only in predisposed individuals, but also for sporadic cases.

This state-of-the art research Handbook provides a comprehensive, coherent, current synthesis of the empirical and theoretical research concerning teaching and learning in science and lays down a foundation upon which future research can be built. The contributors, all leading experts in their research areas, represent the international and gender diversity that exists in the science education research community. As a whole, the Handbook of Research on Science Education demonstrates that science education is alive and well and illustrates its vitality. It is an essential resource for the entire science education community, including veteran and emerging researchers, university faculty, graduate students, practitioners in the schools, and science education professionals outside of universities. The National Association for Research in Science Teaching (NARST) endorses the Handbook of Research on Science Education as an important and valuable synthesis of the current knowledge in the field of science education by leading individuals in the field. For more information on NARST, please visit: http://www.narst.org/.