Mathematical Models of the Cell and Cell Associated Objects PDF Download
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Author: Viktor V. Ivanov
Publisher: Elsevier
ISBN: 0080462723
Category : Computers
Languages : en
Pages : 355
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Book Description
This book gives the reader a survey of hundreds results in the field of the cell and cell associated objects modeling. Applications to modeling in the areas of AIDS, cancers and life longevity are investigated in this book. - Introduces and proves fundamental properties of evolutionary systems on optimal distribution of their various resources on their internal and external functions - Gives detailed analysis of applications to modeling AIDS, cancers, and life longevity - Introducing and grounding the respective numerical algorithms and software - Detailed analysis of hundreds of scientific works in the field of mathematical modeling of the cell and cell associated objects
Author: Viktor V. Ivanov
Publisher: Elsevier
ISBN: 0080462723
Category : Computers
Languages : en
Pages : 355
Get Book Here
Book Description
This book gives the reader a survey of hundreds results in the field of the cell and cell associated objects modeling. Applications to modeling in the areas of AIDS, cancers and life longevity are investigated in this book. - Introduces and proves fundamental properties of evolutionary systems on optimal distribution of their various resources on their internal and external functions - Gives detailed analysis of applications to modeling AIDS, cancers, and life longevity - Introducing and grounding the respective numerical algorithms and software - Detailed analysis of hundreds of scientific works in the field of mathematical modeling of the cell and cell associated objects
Author: Elizabeth Spencer Allman
Publisher: Cambridge University Press
ISBN: 9780521525862
Category : Mathematics
Languages : en
Pages : 388
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Book Description
This introductory textbook on mathematical biology focuses on discrete models across a variety of biological subdisciplines. Biological topics treated include linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction, genetics, and infectious disease models. The coverage of models of molecular evolution and phylogenetic tree construction from DNA sequence data is unique among books at this level. Computer investigations with MATLAB are incorporated throughout, in both exercises and more extensive projects, to give readers hands-on experience with the mathematical models developed. MATLAB programs accompany the text. Mathematical tools, such as matrix algebra, eigenvector analysis, and basic probability, are motivated by biological models and given self-contained developments, so that mathematical prerequisites are minimal.
Author: Lee A. Segel
Publisher: CUP Archive
ISBN: 9780521229258
Category : Mathematics
Languages : en
Pages : 776
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Book Description
Interest in theoretical biology is rapidly growing and this 1981 book attempts to make the theory more accessible to experimentalists. Its primary purpose is to demonstrate to experimental molecular and cellular biologists the possible usefulness of mathematical models. Biologists with a basic command of calculus should be able to learn from the book what assumptions are implied by various types of equations, to understand in broad outline a number of major theoretical concepts, and to be aware of some of the difficulties connected with analytical and numerical solutions of mathematical problems. Thus they should be able to appreciate the significance of theoretical papers in their fields and to communicate usefully with theoreticians in the course of their work.
Author: Leah Edelstein-Keshet
Publisher: SIAM
ISBN: 9780898719147
Category : Mathematics
Languages : en
Pages : 629
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Book Description
Mathematical Models in Biology is an introductory book for readers interested in biological applications of mathematics and modeling in biology. A favorite in the mathematical biology community, it shows how relatively simple mathematics can be applied to a variety of models to draw interesting conclusions. Connections are made between diverse biological examples linked by common mathematical themes. A variety of discrete and continuous ordinary and partial differential equation models are explored. Although great advances have taken place in many of the topics covered, the simple lessons contained in this book are still important and informative. Audience: the book does not assume too much background knowledge--essentially some calculus and high-school algebra. It was originally written with third- and fourth-year undergraduate mathematical-biology majors in mind; however, it was picked up by beginning graduate students as well as researchers in math (and some in biology) who wanted to learn about this field.
Author: Satya Nanda Vel Arjunan
Publisher: Springer Science & Business Media
ISBN: 146146157X
Category : Science
Languages : en
Pages : 185
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Book Description
The interdisciplinary field of molecular systems biology aims to understand the behavior and mechanisms of biological processes composed of individual molecular components. As we gain more qualitative and quantitative information of complex intracellular processes, biochemical modeling and simulation become indispensable not only to uncover the molecular mechanisms of the processes, but to perform useful predictions. To this end, the E‐Cell System, a multi‐algorithm, multi‐timescale object‐oriented simulation platform, can be used to construct predictive virtual biological systems. Gene regulatory and biochemical networks that constitute a sub‐ or a whole cellular system can be constructed using the E‐Cell System to perform qualitative and quantitative analyses. The purpose of E‐Cell System: Basic Concepts and Applications is to provide a comprehensive guide for the E‐Cell System version 3 in terms of the software features and its usage. While the publicly available E‐Cell Simulation Environment version 3 User's Manual provides the technical details of model building and scripting, it does not describe some of the underlying concepts of the E‐Cell System. The first part of the book addresses this issue by providing the basic concepts of modeling and simulation with the E‐Cell System.
Author: Herold G. Dehling
Publisher: Elsevier
ISBN: 0080548970
Category : Mathematics
Languages : en
Pages : 291
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Book Description
There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. - Introduction to stochastic process modelling as an alternative modelling technique - Shows how stochastic modelling may be succesful where the traditional technique fails - Overview of stochastic modelling in process technology in the research literature - Illustration of the principle by a wide range of practical examples - In-depth and self-contained discussions - Points the way to both mathematical and technological research in a new, rewarding field
Author: Zhi Zong
Publisher: Elsevier
ISBN: 0080463851
Category : Mathematics
Languages : en
Pages : 321
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Book Description
Mixing up various disciplines frequently produces something that are profound and far-reaching. Cybernetics is such an often-quoted example. Mix of information theory, statistics and computing technology proves to be very useful, which leads to the recent development of information-theory based methods for estimating complicated probability distributions. Estimating probability distribution of a random variable is the fundamental task for quite some fields besides statistics, such as reliability, probabilistic risk analysis (PSA), machine learning, pattern recognization, image processing, neural networks and quality control. Simple distribution forms such as Gaussian, exponential or Weibull distributions are often employed to represent the distributions of the random variables under consideration, as we are taught in universities. In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al. Exact estimation of the probability distribution of a random variable is very important. Take stock market prediction for example. Gaussian distribution is often used to model the fluctuations of stock prices. If such fluctuations are not normally distributed, and we use the normal distribution to represent them, how could we expect our prediction of stock market is correct? Another case well exemplifying the necessity of exact estimation of probability distributions is reliability engineering. Failure of exact estimation of the probability distributions under consideration may lead to disastrous designs. There have been constant efforts to find appropriate methods to determine complicated distributions based on random samples, but this topic has never been systematically discussed in detail in a book or monograph. The present book is intended to fill the gap and documents the latest research in this subject. Determining a complicated distribution is not simply a multiple of the workload we use to determine a simple distribution, but it turns out to be a much harder task. Two important mathematical tools, function approximation and information theory, that are beyond traditional mathematical statistics, are often used. Several methods constructed based on the two mathematical tools for distribution estimation are detailed in this book. These methods have been applied by the author for several years to many cases. They are superior in the following senses: (1) No prior information of the distribution form to be determined is necessary. It can be determined automatically from the sample; (2) The sample size may be large or small; (3) They are particularly suitable for computers. It is the rapid development of computing technology that makes it possible for fast estimation of complicated distributions. The methods provided herein well demonstrate the significant cross influences between information theory and statistics, and showcase the fallacies of traditional statistics that, however, can be overcome by information theory. Key Features: - Density functions automatically determined from samples - Free of assuming density forms - Computation-effective methods suitable for PC- density functions automatically determined from samples- Free of assuming density forms- Computation-effective methods suitable for PC
Author: Anatoli Torokhti
Publisher: Elsevier
ISBN: 0080475388
Category : Mathematics
Languages : en
Pages : 413
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Book Description
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation - Non-Lagrange interpolation - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering
Author: Brian P. Ingalls
Publisher: MIT Press
ISBN: 0262545829
Category : Science
Languages : en
Pages : 423
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Book Description
An introduction to the mathematical concepts and techniques needed for the construction and analysis of models in molecular systems biology. Systems techniques are integral to current research in molecular cell biology, and system-level investigations are often accompanied by mathematical models. These models serve as working hypotheses: they help us to understand and predict the behavior of complex systems. This book offers an introduction to mathematical concepts and techniques needed for the construction and interpretation of models in molecular systems biology. It is accessible to upper-level undergraduate or graduate students in life science or engineering who have some familiarity with calculus, and will be a useful reference for researchers at all levels. The first four chapters cover the basics of mathematical modeling in molecular systems biology. The last four chapters address specific biological domains, treating modeling of metabolic networks, of signal transduction pathways, of gene regulatory networks, and of electrophysiology and neuronal action potentials. Chapters 3–8 end with optional sections that address more specialized modeling topics. Exercises, solvable with pen-and-paper calculations, appear throughout the text to encourage interaction with the mathematical techniques. More involved end-of-chapter problem sets require computational software. Appendixes provide a review of basic concepts of molecular biology, additional mathematical background material, and tutorials for two computational software packages (XPPAUT and MATLAB) that can be used for model simulation and analysis.
Author: Jibitesh Mishra
Publisher: Elsevier
ISBN: 0080469388
Category : Computers
Languages : en
Pages : 274
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Book Description
L-System Fractals covers all the fundamental aspects of generating fractals through L-system. Also it provides insight to various researches in this area for generating fractals through L-system approach & estimating dimensions. Also it discusses various applications of L-system fractals. - Fractals generated from L-System including hybrid fractals - Dimension calculation for L-system fractals - Images and codes for L-system fractals - Research directions in the area of L-system fractals - Usage of various freely downloadable tools in this area
