Although the spatial dimension of ecosystem dynamics is now widely recognized, the specific mechanisms behind species patterning in space are still poorly understood and the corresponding theoretical framework is underdeveloped. Going beyond the classical Turing scenario of pattern formation, Spatiotemporal Patterns in Ecology and Epidemiology: Theory, Models, and Simulation illustrates how mathematical modeling and numerical simulations can lead to greater understanding of these issues. It takes a unified approach to population dynamics and epidemiology by presenting several...
Although the spatial dimension of ecosystem dynamics is now widely recognized, the specific mechanisms behind species patterning in space are still po...
Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. It implements the algorithms in Perl and R, two widely used scripting languages in computational biology.
The book provides a well-rounded explanation of traditional issues as well as an...
Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms i...
The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation methods to glean meaningful information in systems-level biology. Applying this approach to the underlying molecular mechanisms of tumorigenesis, cancer researchers can uncover a series of new discoveries and biological insights.
The First Cancer Systems Biology Book Designed for Computational and Experimental Biologists Unusual in its dualistic approach, Cancer Systems Biology discusses...
The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computat...
Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.
New to the Second...
Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clea...
Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations. The authors introduce mathematical and programming tools, along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models.
After introducing...
Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate bot...
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of data across scales, and the prediction of tumor progression and treatment outcome (in silico oncology).
Drawing on an interdisciplinary group of distinguished international experts, Multiscale Cancer...
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques,...
Dynamical Systems for Biological Modeling: An Introduction prepares both biology and mathematics students with the understanding and techniques necessary to undertake basic modeling of biological systems. It achieves this through the development and analysis of dynamical systems.
The approach emphasizes qualitative ideas rather than explicit computations. Some technical details are necessary, but a qualitative approach emphasizing ideas is essential for understanding. The modeling approach helps students focus on essentials rather than extensive mathematical...
Dynamical Systems for Biological Modeling: An Introduction prepares both biology and mathematics students with the understanding a...
Spanning biological, mathematical, computational, and engineering sciences, computational biofluiddynamics addresses a diverse family of problems involving fluid flow inside and around living organisms, organs, tissue, biological cells, and other biological materials. Computational Hydrodynamics of Capsules and Biological Cells provides a comprehensive, rigorous, and current introduction to the fundamental concepts, mathematical formulation, alternative approaches, and predictions of this evolving field.
In the first several chapters on boundary-element,...
Spanning biological, mathematical, computational, and engineering sciences, computational biofluiddynamics addresses a diverse family of problems i...
Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clinical and research datasets. The common computational tasks of medical informatics are accessible to anyone willing to learn the basics.
Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby...
Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional pro...
