Proc Of The Third Brazilian Symp On Mathematical And Computational Biology V1
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BIOMAT 2005
Author | : Rubem Mondaini |
Publisher | : World Scientific |
Total Pages | : 409 |
Release | : 2006 |
Genre | : Science |
ISBN | : 9812567976 |
This volume contains the contributions of the keynote speakers to the BIOMAT 2005 symposium, as well as a collection of selected papers by pioneering researchers. It provides a comprehensive review of the mathematical modeling of cancer development, Alzheimer's disease, malaria, and aneurysm development. Various models for the immune system and epidemiological issues are analyzed and reviewed. The book also explores protein structure prediction by optimization and combinatorial techniques (Steiner trees). The coverage includes bioinformatics issues, regulation of gene expression, evolution, development, DNA and array modeling, and small world networks.
Modeling Life
Author | : Alan Garfinkel |
Publisher | : Springer |
Total Pages | : 456 |
Release | : 2017-09-06 |
Genre | : Mathematics |
ISBN | : 3319597310 |
This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science?
Mathematical Biology
Author | : Ronald W. Shonkwiler |
Publisher | : Springer Science & Business Media |
Total Pages | : 552 |
Release | : 2009-08-04 |
Genre | : Science |
ISBN | : 0387709843 |
This text presents mathematical biology as a field with a unity of its own, rather than only the intrusion of one science into another. The book focuses on problems of contemporary interest, such as cancer, genetics, and the rapidly growing field of genomics.
Biological Modeling and Simulation
Author | : Russell Schwartz |
Publisher | : MIT Press |
Total Pages | : 403 |
Release | : 2008-07-25 |
Genre | : Science |
ISBN | : 0262303396 |
A practice-oriented survey of techniques for computational modeling and simulation suitable for a broad range of biological problems. There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives the aspiring student a bag of tricks that will serve him or her well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered. The text, which grew out of a class taught at Carnegie Mellon University, covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology.
Persistence Theory: From Quiver Representations to Data Analysis
Author | : Steve Y. Oudot |
Publisher | : American Mathematical Soc. |
Total Pages | : 229 |
Release | : 2017-05-17 |
Genre | : Mathematics |
ISBN | : 1470434431 |
Persistence theory emerged in the early 2000s as a new theory in the area of applied and computational topology. This book provides a broad and modern view of the subject, including its algebraic, topological, and algorithmic aspects. It also elaborates on applications in data analysis. The level of detail of the exposition has been set so as to keep a survey style, while providing sufficient insights into the proofs so the reader can understand the mechanisms at work. The book is organized into three parts. The first part is dedicated to the foundations of persistence and emphasizes its connection to quiver representation theory. The second part focuses on its connection to applications through a few selected topics. The third part provides perspectives for both the theory and its applications. The book can be used as a text for a course on applied topology or data analysis.
Bayesian Evolutionary Analysis with BEAST
Author | : Alexei J. Drummond |
Publisher | : Cambridge University Press |
Total Pages | : 263 |
Release | : 2015-08-06 |
Genre | : Science |
ISBN | : 1316298345 |
What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: • Addresses the theoretical aspects of the field • Advises on how to prepare and perform phylogenetic analysis • Helps with interpreting analyses and visualisation of phylogenies • Describes the software architecture • Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users – from those using phylogenetic tools, to computational biologists and Bayesian statisticians.