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Computational modeling has become an essential tool for researchers in the biological sciences. Yet in biological modeling, there is no one technique that is suitable for all problems. Instead, different problems call for different approaches. Furthermore, it can be helpful to analyze the same system using a variety of approaches, to be able to exploit the advantages and drawbacks of each. In practice, it is often unclear which modeling approaches will be most suitable for a particular biological question - a problem that requires researchers to know a reasonable amount about a number of techniques, rather than become experts on a single one. Introduction to Modeling for Biosciences addresses this issue by presenting a broad overview of the most important techniques used to model biological systems. In addition to providing an introduction into the use of a wide range of software tools and modeling environments, this helpful text/reference describes the constraints and difficulties that each modeling technique presents in practice. This enables the researcher to quickly determine which software package would be most useful for their particular problem. Topics and features: Introduces a basic array of techniques to formulate models of biological systems, and to solve them Discusses agent-based models, stochastic modeling techniques, differential equations and Gillespie's stochastic simulation algorithm Intersperses the text with exercises Includes practical introductions to the Maxima computer algebra system, the PRISM model checker, and the Repast Simphony agent modeling environment Contains appendices on Repast batch running, rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts Supplies source code for many of the example models discussed, at the associated website http://www.cs.kent.ac.uk/imb/ This unique and practical work guides the novice modeler through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model. Students and active researchers in the biosciences will also benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book, as well as thorough descriptions and examples. David J. Barnes is a lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming. Dominique Chu is a lecturer in computer science at the University of Kent, UK. He is an expert in mathematical and computational modeling of biological systems, with years of experience in these subject fields.

Biomathematics. Biometry. Biostatistics

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Probability, Statistics and Modelling in Public Health consists of refereed contributions by expert biostatisticians that discuss various probabilistic and statistical models used in public health. Many of them are based on the work of Marvin Zelen of the Harvard School of Public Health. Topics discussed include models based on Markov and semi-Markov processes, multi-state models, models and methods in lifetime data analysis, accelerated failure models, design and analysis of clinical trials, Bayesian methods, pharmaceutical and environmental statistics, degradation models, epidemiological methods, screening programs, early detection of diseases, and measurement and analysis of quality of life. Audience This book is intended for researchers interested in statistical methodology in the biomedical field.

Biomathematics. Biometry. Biostatistics --- medische statistiek --- biostatistiek --- biometrie

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Survival analysis, the analysis of failure time data, is a rapid developing area and a number of books on the topic have been published in last twenty-five years. However, all of these books deal with right-censored failure time data, not the analysis of interval-censored failure time data. Interval-censored data include right-censored data as a special case and occur in many fields. The analysis of interval-censored data is much more difficult than that of right-censored data because the censoring mechanism that yields interval censoring is more complicated than that for right censoring. This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. A number of inference approaches are discussed in the book, including the maximum likelihood, estimating equations, sieve maximum likelihood, and conditional likelihood. One major difference between the analyses of right- and interval-censored data is that the theory of counting processes, which is responsible for substantial advances in the theory and development of modern statistical methods for right-censored data, is not applicable to interval-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. In addition, Bayesian methods and the analysis of interval-censored data with informative interval censoring are considered as well as the analysis of interval-censored recurrent event, or panel count, data. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions. It can also be used as a text for a graduate course in statistics or biostatistics that assume a basic knowledge of probability and statistics. Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri-Columbia. He has developed novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last fifteen years.

Biomathematics. Biometry. Biostatistics --- medische statistiek --- biostatistiek --- biometrie

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Although there have been significant developments in the field of biometrics in recent years, much of the research in this area has focused upon the front-end technology. This unique Guide to Biometrics for Large-Scale Systems considers biometric technology in a broader light, integrating the concept more seamlessly into mainstream Information Technology, while also striving to understand the cultural attitudes and the societal impact of identity management. This approach represents a step-change in current thinking - that may come to be viewed as a milestone in the development of biometric technology - in which many established tenets are placed under considerable scrutiny. Topics and features: Summarizes the material covered at the beginning of every chapter, and provides chapter-ending review questions and discussion points Reviews identity verification in nature, and early historical interest in anatomical measurement, which form the foundations of the modern field of biometrics Provides an overview of biometric technology, presents a focus on biometric systems and true systems integration, examines the concept of identity management, and predicts future trends Investigates performance issues in biometric systems, the management and security of biometric data, and the impact of mobile devices on biometrics technology Explains the equivalence of performance across operational nodes and why this is so important, introducing the APEX system as an example of how this issue can be addressed Considers the legal, political and societal factors of biometric technology, in addition to user psychology and other human factors This highly practical reference/guidebook is an invaluable resource for program managers, application developers and consultants working in this area. Students interested in biometric technology will also find this a must-read. Julian Ashbourn is an experienced and successful author on topics ranging from computer science and identity management through to geoscience and the natural sciences in general. His publications include the Springer titles Practical Biometrics: From Aspiration to Implementation, BANTAM User Guide: Biometric and Token Technology Application Modeling Language and Geological Landscapes of Britain.

Biomathematics. Biometry. Biostatistics --- biomathematica --- biostatistiek --- biometrie

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In survival analysis there has long been a need for models that goes beyond the Cox model as the proportional hazards assumption often fails in practice. This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the specific aim of describing time-varying effects of explanatory variables. One model that receives special attention is Aalen's additive hazards model that is particularly well suited for dealing with time-varying effects. The book covers the use of residuals and resampling techniques to assess the fit of the models and also points out how the suggested models can be utilised for clustered survival data. The authors demonstrate the practically important aspect of how to do hypothesis testing of time-varying effects making backwards model selection strategies possible for the flexible models considered. The use of the suggested models and methods is illustrated on real data examples. The methods are available in the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets. This gives the reader a unique chance of obtaining hands-on experience. This book is well suited for statistical consultants as well as for those who would like to see more about the theoretical justification of the suggested procedures. It can be used as a textbook for a graduate/master course in survival analysis, and students will appreciate the exercises included after each chapter. The applied side of the book with many worked examples accompanied with R-code shows in detail how one can analyse real data and at the same time gives a deeper understanding of the underlying theory. Torben Martinussen is at the Department of Natural Sciences at the Royal Veterinary and Agricultural University. He has a Ph.D. from University of Copenhagen and is associate editor of the Scandinavian Journal of Statistics. Thomas Scheike is at the Department of Biostatistics at University of Copenhagen. He has a Ph.D. from University of California at Berkeley and is Doctor of Science at the University of Copenhagen. He is the editor of the Scandinavian Journal of Statistics and associate editor of several other journals.

Biomathematics. Biometry. Biostatistics --- medische statistiek --- biostatistiek --- biometrie