Mathematical Immunology of Virus Infections

This book PDF is perfect for those who love Mathematics genre, written by Gennady Bocharov and published by Springer which was released on 12 June 2018 with total hardcover pages 256. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Mathematical Immunology of Virus Infections books below.

Mathematical Immunology of Virus Infections

Author	: Gennady Bocharov
File Size	: 46,7 Mb
Publisher	: Springer
Language	: English
Release Date	: 12 June 2018
ISBN	: 9783319723174
Pages	: 256 pages

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Mathematical Immunology of Virus Infections by Gennady Bocharov Book PDF Summary

This monograph concisely but thoroughly introduces the reader to the field of mathematical immunology. The book covers first basic principles of formulating a mathematical model, and an outline on data-driven parameter estimation and model selection. The authors then introduce the modeling of experimental and human infections and provide the reader with helpful exercises. The target audience primarily comprises researchers and graduate students in the field of mathematical biology who wish to be concisely introduced into mathematical immunology.

Mathematical Immunology of Virus Infections by Gennady Bocharov,Vitaly Volpert,Burkhard Ludewig,Andreas Meyerhans

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Killer Cell Dynamics by Dominik Wodarz

This book reviews how mathematical and computational approaches can be useful to help us understand how killer T-cell responses work to fight viral infections. It also demonstrates, in a writing style that exemplifies the point, that such mathematical and computational approaches are most valuable when coupled with experimental work through

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Virus Dynamics Mathematical Principles of Immunology and Virology by Martin Nowak,Robert M. May

This groundbreaking book describes the emerging field of theoretical immunology, in particular the use of mathematical models to describe the spread of infectious diseases within patients. It reveals fascinating insights into the dynamics of viral and other infections, and the interactions between infectious agents and immune responses. Structured around the

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Virus Dynamics by Martin A. Nowak,Robert M. May

This text describes the emerging field of theoretical immunology, in particular the use of mathematical models to describe the spread of infectious diseases within patients. It reveals insights into the dynamics of viral & other infections.

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Mathematical Modelling of Immune Response in Infectious Diseases by Guri I. Marchuk

Beginning his work on the monograph to be published in English, this author tried to present more or less general notions of the possibilities of mathematics in the new and rapidly developing science of infectious immunology, describing the processes of an organism's defence against antigen invasions. The results presented in

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Mathematical Modeling of the Immune System in Homeostasis Infection and Disease by Gennady Bocharov,Burkhard Ludewig,Andreas Meyerhans,Vitaly Volpert

The immune system provides the host organism with defense mechanisms against invading pathogens and tumor development and it plays an active role in tissue and organ regeneration. Deviations from the normal physiological functioning of the immune system can lead to the development of diseases with various pathologies including autoimmune diseases

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Mathematical Methods in Immunology by Jerome Kenneth Percus

Any organism, to survive, must use a variety of defense mechanisms. A relatively recent evolutionary development is that of the adaptive immune system, carried to a quite sophisticated level by mammals. The complexity of this system calls for its encapsulation by mathematical models, and this book aims at the associated

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Mathematical Models and Immune Cell Biology by Carmen Molina-París,Grant Lythe

Whole new areas of immunological research are emerging from the analysis of experimental data, going beyond statistics and parameter estimation into what an applied mathematician would recognise as modelling of dynamical systems. Stochastic methods are increasingly important, because stochastic models are closer to the Brownian reality of the cellular and

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