Author: Meryl E. Wastney
Publisher: Academic Press
ISBN: 0127367403
Category : Computers
Languages : en
Pages : 401
Book Description
Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine. Contains practical descriptions of how to analyze kinetic data Provides examples of how to develop and use models Describes several software packages including SAAM/CONSAM Includes software with working models
Investigating Biological Systems Using Modeling
Author: Meryl E. Wastney
Publisher: Academic Press
ISBN: 0127367403
Category : Computers
Languages : en
Pages : 401
Book Description
Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine. Contains practical descriptions of how to analyze kinetic data Provides examples of how to develop and use models Describes several software packages including SAAM/CONSAM Includes software with working models
Publisher: Academic Press
ISBN: 0127367403
Category : Computers
Languages : en
Pages : 401
Book Description
Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine. Contains practical descriptions of how to analyze kinetic data Provides examples of how to develop and use models Describes several software packages including SAAM/CONSAM Includes software with working models
Modeling Biological Systems:
Author: James W. Haefner
Publisher: Springer Science & Business Media
ISBN: 9780387250113
Category : Science
Languages : en
Pages : 500
Book Description
I Principles 1 1 Models of Systems 3 1. 1 Systems. Models. and Modeling . . . . . . . . . . . . . . . . . . . . 3 1. 2 Uses of Scientific Models . . . . . . . . . . . . . . . . . . . . . . . . 4 1. 3 Example: Island Biogeography . . . . . . . . . . . . . . . . . . . . . 6 1. 4 Classifications of Models . . . . . . . . . . . . . . . . . . . . . . . . 10 1. 5 Constraints on Model Structure . . . . . . . . . . . . . . . . . . . . . 12 1. 6 Some Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1. 7 Misuses of Models: The Dark Side . . . . . . . . . . . . . . . . . . . 13 1. 8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 The Modeling Process 17 2. 1 Models Are Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. 2 Two Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . 18 2. 3 An Example: Population Doubling Time . . . . . . . . . . . . . . . . 24 2. 4 Model Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. 5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Qualitative Model Formulation 32 3. 1 How to Eat an Elephant . . . . . . . . . . . . . . . . . . . . . . . . . 32 3. 2 Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3. 3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3. 4 Errors in Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . 44 3. 5 Advantages and Disadvantages of Forrester Diagrams . . . . . . . . . 44 3. 6 Principles of Qualitative Formulation . . . . . . . . . . . . . . . . . . 45 3. 7 Model Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. 8 Other Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . . 49 viii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 9 Exercises 53 4 Quantitative Model Formulation: I 4. 1 From Qualitative to Quantitative . . . . . . . . . . . . . . . . . Finite Difference Equations and Differential Equations 4. 2 . . . . . . . . . . . . . . . . 4. 3 Biological Feedback in Quantitative Models . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 4 Example Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 5 Exercises 5 Quantitative Model Formulation: I1 81 . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Physical Processes 81 . . . . . . . . . . . . . . . 5. 2 Using the Toolbox of Biological Processes 89 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3 Useful Functions 96 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 4 Examples 102 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5 Exercises 104 6 Numerical Techniques 107 . . . . . . . . . . . . . . . . . . . . . . . 6. 1 Mistakes Computers Make 107 . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 2 Numerical Integration 110 . . . . . . . . . . . . . . . . 6. 3 Numerical Instability and Stiff Equations 115 . . . . . . . . . . . . . .
Publisher: Springer Science & Business Media
ISBN: 9780387250113
Category : Science
Languages : en
Pages : 500
Book Description
I Principles 1 1 Models of Systems 3 1. 1 Systems. Models. and Modeling . . . . . . . . . . . . . . . . . . . . 3 1. 2 Uses of Scientific Models . . . . . . . . . . . . . . . . . . . . . . . . 4 1. 3 Example: Island Biogeography . . . . . . . . . . . . . . . . . . . . . 6 1. 4 Classifications of Models . . . . . . . . . . . . . . . . . . . . . . . . 10 1. 5 Constraints on Model Structure . . . . . . . . . . . . . . . . . . . . . 12 1. 6 Some Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1. 7 Misuses of Models: The Dark Side . . . . . . . . . . . . . . . . . . . 13 1. 8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 The Modeling Process 17 2. 1 Models Are Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. 2 Two Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . 18 2. 3 An Example: Population Doubling Time . . . . . . . . . . . . . . . . 24 2. 4 Model Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. 5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Qualitative Model Formulation 32 3. 1 How to Eat an Elephant . . . . . . . . . . . . . . . . . . . . . . . . . 32 3. 2 Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3. 3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3. 4 Errors in Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . 44 3. 5 Advantages and Disadvantages of Forrester Diagrams . . . . . . . . . 44 3. 6 Principles of Qualitative Formulation . . . . . . . . . . . . . . . . . . 45 3. 7 Model Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. 8 Other Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . . 49 viii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 9 Exercises 53 4 Quantitative Model Formulation: I 4. 1 From Qualitative to Quantitative . . . . . . . . . . . . . . . . . Finite Difference Equations and Differential Equations 4. 2 . . . . . . . . . . . . . . . . 4. 3 Biological Feedback in Quantitative Models . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 4 Example Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 5 Exercises 5 Quantitative Model Formulation: I1 81 . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Physical Processes 81 . . . . . . . . . . . . . . . 5. 2 Using the Toolbox of Biological Processes 89 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3 Useful Functions 96 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 4 Examples 102 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5 Exercises 104 6 Numerical Techniques 107 . . . . . . . . . . . . . . . . . . . . . . . 6. 1 Mistakes Computers Make 107 . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 2 Numerical Integration 110 . . . . . . . . . . . . . . . . 6. 3 Numerical Instability and Stiff Equations 115 . . . . . . . . . . . . . .
Mathematical Modeling in Nutrition and the Health Sciences
Author: Janet A. Novotny
Publisher: Springer Science & Business Media
ISBN: 1441990194
Category : Computers
Languages : en
Pages : 437
Book Description
This volume is the proceedings of the 7th Mathematical Modeling in Experimental Nutrition Conference held at Penn State University July 29 until August 1, 2000. The book addresses the determination of optimal intakes of nutrients and food components to provide lifelong health and reduce incidence of disease. Mathematical modelling provides a means of rigorously defining the functions of a system and using a variety of conditions to stimulate responses. This volume presents the newest advances in modelling and related experimental techniques required to meet the new challenges currently facing nutrition and biological science.
Publisher: Springer Science & Business Media
ISBN: 1441990194
Category : Computers
Languages : en
Pages : 437
Book Description
This volume is the proceedings of the 7th Mathematical Modeling in Experimental Nutrition Conference held at Penn State University July 29 until August 1, 2000. The book addresses the determination of optimal intakes of nutrients and food components to provide lifelong health and reduce incidence of disease. Mathematical modelling provides a means of rigorously defining the functions of a system and using a variety of conditions to stimulate responses. This volume presents the newest advances in modelling and related experimental techniques required to meet the new challenges currently facing nutrition and biological science.
Investigating Complex Phenomena: Bridging between Systems Thinking and Modeling in Science Education
Author: Tom Bielik
Publisher: Frontiers Media SA
ISBN: 2832539017
Category : Education
Languages : en
Pages : 186
Book Description
Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. When investigating complex phenomena, such as climate change or pandemic outbreaks, students are expected to engage in systems thinking by considering the boundaries of the investigated system, identifying the relevant components and their interactions, and exploring system attributes such as hierarchical organization, dynamicity, feedback loops, and emergence. Scientific models are tools that support students’ reasoning and understanding of complex systems, and students are expected to develop their modeling competence and to engage in the modeling process by constructing, testing, revising, and using models to explain and predict phenomena. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena.
Publisher: Frontiers Media SA
ISBN: 2832539017
Category : Education
Languages : en
Pages : 186
Book Description
Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. When investigating complex phenomena, such as climate change or pandemic outbreaks, students are expected to engage in systems thinking by considering the boundaries of the investigated system, identifying the relevant components and their interactions, and exploring system attributes such as hierarchical organization, dynamicity, feedback loops, and emergence. Scientific models are tools that support students’ reasoning and understanding of complex systems, and students are expected to develop their modeling competence and to engage in the modeling process by constructing, testing, revising, and using models to explain and predict phenomena. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena.
Modeling Dynamic Biological Systems
Author: Bruce Hannon
Publisher: Springer Science & Business Media
ISBN: 1461206510
Category : Science
Languages : en
Pages : 399
Book Description
Models help us understand the dynamics of real-world processes by using the computer to mimic the actual forces that are known or assumed to result in a system's behavior. This book does not require a substantial background in mathematics or computer science.
Publisher: Springer Science & Business Media
ISBN: 1461206510
Category : Science
Languages : en
Pages : 399
Book Description
Models help us understand the dynamics of real-world processes by using the computer to mimic the actual forces that are known or assumed to result in a system's behavior. This book does not require a substantial background in mathematics or computer science.
Mathematical Models in Biology
Author: Leah Edelstein-Keshet
Publisher: SIAM
ISBN: 9780898719147
Category : Mathematics
Languages : en
Pages : 629
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.
Publisher: SIAM
ISBN: 9780898719147
Category : Mathematics
Languages : en
Pages : 629
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.
Mathematical Models in Biology
Author: Elizabeth Spencer Allman
Publisher: Cambridge University Press
ISBN: 9780521525862
Category : Mathematics
Languages : en
Pages : 388
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.
Publisher: Cambridge University Press
ISBN: 9780521525862
Category : Mathematics
Languages : en
Pages : 388
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.
Handbook of Toxicogenomics
Author: Jürgen Borlak
Publisher: John Wiley & Sons
ISBN: 3527604510
Category : Science
Languages : en
Pages : 705
Book Description
Toxicogenomics is a new, dynamic and very promising field that can help optimize toxicity analyses and streamline research into active substances. It is of interest not only for basic research and development, but also from a legal and ethical perspective. Here, experts from all the fields mentioned will find solid information provided by an international team of experienced authors. With its approach as an interdisciplinary overview, it will prove particularly useful for all those needing to develop appropriate research strategies. The authors work for major research institutions, such as the Fraunhofer Institute of Toxicology and Experimental Medicine (Germany), the German Cancer Research Center, the National Institute of Environmental Health Science (USA), the National Institute of Health Science (Japan) or for companies like Affymetrix, Altana Pharma, Bayer, Boehringer Ingelheim, Bruker, Merck, Nimblegen, Novartis, and Syngenta. Coverage ranges from the technology platforms applied, including DNA arrays or proteomics, via the bioinformatics tools required, right up to applications of toxicogenomics presented in numerous case studies, while also including an overview of national programs and initiatives as well as regulatory perspectives. Walter Rosenthal, Director of the Research Institute for Molecular Pharmacology in Berlin, praises the book thus: "I would like to congratulate the publishers of this handbook, one that deals with a extremely hot topic. They have succeeded in gaining as authors leading representatives from this field. The Handbook impressively shows how modern genomic research is leading to rapid advances and new insights within toxicology."
Publisher: John Wiley & Sons
ISBN: 3527604510
Category : Science
Languages : en
Pages : 705
Book Description
Toxicogenomics is a new, dynamic and very promising field that can help optimize toxicity analyses and streamline research into active substances. It is of interest not only for basic research and development, but also from a legal and ethical perspective. Here, experts from all the fields mentioned will find solid information provided by an international team of experienced authors. With its approach as an interdisciplinary overview, it will prove particularly useful for all those needing to develop appropriate research strategies. The authors work for major research institutions, such as the Fraunhofer Institute of Toxicology and Experimental Medicine (Germany), the German Cancer Research Center, the National Institute of Environmental Health Science (USA), the National Institute of Health Science (Japan) or for companies like Affymetrix, Altana Pharma, Bayer, Boehringer Ingelheim, Bruker, Merck, Nimblegen, Novartis, and Syngenta. Coverage ranges from the technology platforms applied, including DNA arrays or proteomics, via the bioinformatics tools required, right up to applications of toxicogenomics presented in numerous case studies, while also including an overview of national programs and initiatives as well as regulatory perspectives. Walter Rosenthal, Director of the Research Institute for Molecular Pharmacology in Berlin, praises the book thus: "I would like to congratulate the publishers of this handbook, one that deals with a extremely hot topic. They have succeeded in gaining as authors leading representatives from this field. The Handbook impressively shows how modern genomic research is leading to rapid advances and new insights within toxicology."
Natural Computing for Simulation-Based Optimization and Beyond
Author: Silja Meyer-Nieberg
Publisher: Springer
ISBN: 3030262154
Category : Business & Economics
Languages : en
Pages : 67
Book Description
This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other. Since natural computing methods have been applied with great success in several application areas, a review concerning potential benefits and pitfalls for simulation studies is merited. The brief presents such an overview and combines it with an introduction to natural computing and selected major approaches, as well as with a concise treatment of general simulation-based optimization. As such, it is the first review which covers both the methodological background and recent application cases. The brief is intended to serve two purposes: First, it can be used to gain more information concerning natural computing, its major dialects, and their usage for simulation studies. It also covers the areas of multi-objective optimization and neuroevolution. While the latter is only seldom mentioned in connection with simulation studies, it is a powerful potential technique. Second, the reader is provided with an overview of several areas of simulation-based optimization which range from logistic problems to engineering tasks. Additionally, the brief focuses on the usage of surrogate and meta-models. The brief presents recent application examples.
Publisher: Springer
ISBN: 3030262154
Category : Business & Economics
Languages : en
Pages : 67
Book Description
This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other. Since natural computing methods have been applied with great success in several application areas, a review concerning potential benefits and pitfalls for simulation studies is merited. The brief presents such an overview and combines it with an introduction to natural computing and selected major approaches, as well as with a concise treatment of general simulation-based optimization. As such, it is the first review which covers both the methodological background and recent application cases. The brief is intended to serve two purposes: First, it can be used to gain more information concerning natural computing, its major dialects, and their usage for simulation studies. It also covers the areas of multi-objective optimization and neuroevolution. While the latter is only seldom mentioned in connection with simulation studies, it is a powerful potential technique. Second, the reader is provided with an overview of several areas of simulation-based optimization which range from logistic problems to engineering tasks. Additionally, the brief focuses on the usage of surrogate and meta-models. The brief presents recent application examples.
Large-Scale Ecology: Model Systems to Global Perspectives
Author:
Publisher: Academic Press
ISBN: 0128110953
Category : Science
Languages : en
Pages : 690
Book Description
Advances in Ecological Research is one of the most successful series in the highly competitive field of ecology. This thematic volume focuses on large scale ecology, publishing important reviews that contribute to our understanding of the field. - Presents the most updated information on the field of large scale ecology, publishing topical and important reviews - Provides all information that relates to a thorough understanding of the field - Includes data on physiology, populations, and communities of plants and animals
Publisher: Academic Press
ISBN: 0128110953
Category : Science
Languages : en
Pages : 690
Book Description
Advances in Ecological Research is one of the most successful series in the highly competitive field of ecology. This thematic volume focuses on large scale ecology, publishing important reviews that contribute to our understanding of the field. - Presents the most updated information on the field of large scale ecology, publishing topical and important reviews - Provides all information that relates to a thorough understanding of the field - Includes data on physiology, populations, and communities of plants and animals