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Kos deg med ubegrenset tilgang til mer enn 600 000 titler.

Lytt og les ubegrenset

Steven F. Railsback

Volker Grimm

Individual-based models are an exciting and widely used new tool for ecology. These computational models allow scientists to explore the mechanisms through which population and ecosystem ecology arises from how individuals interact with each other and their environment. This book provides the first in-depth treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call "individual-based ecology.?

 Grimm and Railsback start with a general primer on modeling: how to design models that are as simple as possible while still allowing specific problems to be solved, and how to move efficiently through a cycle of pattern-oriented model design, implementation, and analysis. Next, they address the problems of theory and conceptual framework for individual-based ecology: What is "theory"? That is, how do we develop reusable models of how system dynamics arise from characteristics of individuals? What conceptual framework do we use when the classical differential equation framework no longer applies? An extensive review illustrates the ecological problems that have been addressed with individual-based models. The authors then identify how the mechanics of building and using individual-based models differ from those of traditional science, and provide guidance on formulating, programming, and analyzing models. This book will be helpful to ecologists interested in modeling, and to other scientists interested in agent-based modeling.

Princeton University Press

Princeton Series in Theoretical and Computational Biology

Individual-based Modeling and Ecology

Freddy Bugge Christiansen

This textbook provides an authoritative introduction to both classical and coalescent approaches to population genetics. Written for graduate students and advanced undergraduates by one of the world's leading authorities in the field, the book focuses on the theoretical background of population genetics, while emphasizing the close interplay between theory and empiricism. Traditional topics such as genetic and phenotypic variation, mutation, migration, and linkage are covered and advanced by contemporary coalescent theory, which describes the genealogy of genes in a population, ultimately connecting them to a single common ancestor. Effects of selection, particularly genomic effects, are discussed with reference to molecular genetic variation. The book is designed for students of population genetics, bioinformatics, evolutionary biology, molecular evolution, and theoretical biology--as well as biologists, molecular biologists, breeders, biomathematicians, and biostatisticians.

 • Contains up-to-date treatment of key areas in classical and modern theoretical population genetics 

 • Provides in-depth coverage of coalescent theory 

 • Discusses genomic effects of selection 

 • Gives examples from empirical population genetics 

 • Incorporates figures, diagrams, and boxed features throughout 

 • Includes end-of-chapter exercises 

 • Speaks to a wide range of students in biology, bioinformatics, and biostatistics

Theories of Population Variation in Genes and Genomes

Odo Diekmann

Hans Heesterbeek

Tom Britton

Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods.

Mathematical Tools for Understanding Infectious Disease Dynamics fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided.

 • Covers the latest research in mathematical modeling of infectious disease epidemiology
 • Integrates deterministic and stochastic approaches
 • Teaches skills in model construction, analysis, inference, and interpretation
 • Features numerous exercises and their detailed elaborations
 • Motivated by real-world applications throughout

Mathematical Tools for Understanding Infectious Disease Dynamics