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Cell culture based research is important for our understanding of biological processes at the cellular and molecular level. Using this approach, the previous decades have produced a wealth of mechanistic information in all areas of biomedical research. Such in vitro research, however, lacks the complexity of in vivo investigations, where many different cell types interact with each other in a normal, three-dimensional environment, with normal levels of cytokines and growth factors. Furthermore, complex human diseases, such as cancer, diabetes or chronic inflammation, can only be modeled in vivo. Due to its small size, its short reproduction time, and the possibility to introduce specific gene mutations, the mouse has become the favourite mammalian model organism to study in vivo function of genes during development and in disease. This book combines review articles on selected subjects presented at the symposium "Mouse as a Model Organism - From Animals to Cells", held in Rovaniemi, Finland, 2009. Among other topics, high-throughput phenotyping of mouse mutants, mouse phenotypes dependent on nature and nuture, and a spectrum of in vivo, ex vivo and in vitro methods to study cancer in mice are described. This book will give an excellent introduction to scientists interested in the use of mice as a model to understand complex biological questions in the post-genomic era. It will highlight the possibilities, but also discuss the current problems and shortcomings, to give a realistic view of the current state-of-art in this fascinating field of biomedical research.
This book is a How To guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in Boxes and Appendices. Self-contained R code which replicates all of the figures and calculations within the text is available to readers on GitHub.
Stephen P. Ellner is Horace White Professor of Ecology and Evolutionary Biology at Cornell University, USA; Dylan Z. Childs is Lecturer and NERC Postdoctoral Fellow in the Department of Animal and Plant Sciences at The University of Sheffield, UK; Mark Rees is Professor in the Department of Animal and Plant Sciences at The University of Sheffield, UK.
<b>Praise for <i>Modeling for Insight</i></b> <p> "Most books on modeling are either too theoretical or too focused on the mechanics of programming. Powell and Batt's emphasis on using simple spreadsheet models to gain business insight (which is, after all, the name of the game) is what makes this book stand head and shoulders above the rest. This clear and practical book deserves a place on the shelf of every business analyst."<br> —Jonathan Koomey, PhD, Lawrence Berkeley National Laboratory and Stanford University, author of <i>Turning Numbers into Knowledge: Mastering the Art of Problem Solving</i> <p> Most business analysts are familiar with using spreadsheets to organize data and build routine models. However, analysts often struggle when faced with examining new and ill-structured problems. <i>Modeling for Insight</i> is a one-of-a-kind guide to building effective spreadsheet models and using them to generate insights. With its hands-on approach, this book provides readers with an effective modeling process and specific modeling tools to become a master modeler. <p> The authors provide a structured approach to problem-solving using four main steps: frame the problem, diagram the problem, build a model, and generate insights. Extensive examples, graduated in difficulty, help readers to internalize this modeling process, while also demonstrating the application of important modeling tools, including: <ul> <li> <p> Influence diagrams <li> <p> Spreadsheet engineering <li> <p> Parameterization <li> <p> Sensitivity analysis <li> <p> Strategy analysis <li> <p> Iterative modeling </ul> <p> The real-world examples found in the book are drawn from a wide range of fields such as financial planning, insurance, pharmaceuticals, advertising, and manufacturing. Each chapter concludes with a discussion on how to use the insights drawn from these models to create an effective business presentation. Microsoft Office Excel and PowerPoint are used throughout the book, along with the add-ins Premium Solver, Crystal Ball, and Sensitivity Toolkit. Detailed appendices guide readers through the use of these software packages, and the spreadsheet models discussed in the book are available to download via the book's related Web site. <i>Modeling for Insight</i> is an ideal book for courses in engineering, operations research, and management science at the upper-undergraduate and graduate levels. It is also a valuable resource for consultants and business analysts who often use spreadsheets to better understand complex problems.
How did ancient peoples explain thunderstorms? How does the Sun send ocean water up into the clouds? How do electric charges form in a storm cloud? This series explores the causes and effects that shape our world. From the underwater volcanoes that sprout into islands, to the rushing waterfalls that spark electric currents, this series demonstrates how both natural and man-made phenomena occur.
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