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The Resource A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource)
A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource)
Resource Information
The item A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 Mathematical Modeling and Immunology An enormous amount of human effort and economic resources has been directed in this century to the fight against cancer. The purpose, of course, has been to find strategies to overcome this hard, challenging and seemingly endless struggle. We can readily imagine that even greater efforts will be required in the next century. The hope is that ultimately humanity will be successful; success will have been achieved when it is possible to activate and control the immune system in its competition against neoplastic cells. Dealing with the abovementioned problem requires the fullest pos sible cooperation among scientists working in different fields: biology, im munology, medicine, physics and, we believe, mathematics. Certainly, bi ologists and immunologists will make the greatest contribution to the re search. However, it is now increasingly recognized that mathematics and computer science may well able to make major contributions to such prob lems. We cannot expect mathematicians alone to solve fundamental prob lems in immunology and (in particular) cancer research, but valuable sup port, however modest, can be provided by mathematicians to the research aspirations of biologists and immunologists working in this field
 Language
 eng
 Extent
 1 online resource (xvi, 344 pages).
 Contents

 1. A Brief History of Immunologic Thinking: Is it Time for Yin and Yang?
 1.1 Koch's Postulates
 1.2 Aristotle's Laws of Logical Argument
 1.3 Antigens and TCell Responses
 1.4 Thinking About the Immune System
 1.5 Fuzzy TCell Model
 1.6 Acknowledgment
 2. General Aspects of Modeling Tumor Growth and Immune Response
 2.1 Introduction
 2.2 What is a Mathematical Model?
 2.3 Introduction to Deterministic Tumor (or Spheroid) Growth Models
 2.4 A PredatorPrey Approach
 2.5 A Model of Tumor Cell/Immune System Interaction
 2.6 Models, Metaphors and Similes: Some Alternative Paradigms
 2.7 References
 2.8 Appendices
 3. Mathematical Modeling of Tumor Growth Kinetics
 3.1 Introduction
 3.2 Tumor Growth as a Dynamical System
 3.3 The Gompertz Model
 3.4 The Logistic Model
 3.5 Models of von Bertalanffy
 3.6 Tumor Growth Modeled by Specific Mechanisms
 3.7 Mathematical Models and Measured Growth Curves
 3.8 Concluding Outlooks
 3.9 Acknowledgment
 3.10 References
 4. Tumor Immune System Interactions: The Kinetic Cellular Theory
 4.1 Introduction
 4.2 A Concise Guide to the Literature
 4.3 Guidelines: From Observation to Simulation
 4.4 Cell Population and Activity
 4.5 Modeling Cell Interactions
 4.6 Evolution Kinetic Equations
 4.7 Experimental Activity
 4.8 Simulation and Validation Problems
 4.9 Remarks Addressed to Applied Mathematicians
 4.10 Perspectives
 4.11 References
 5. From Mutation to Metastasis: The Mathematical Modelling of the Stages of Tumour Development
 5.1 Introduction
 5.2 Avascular Tumour Growth: The Multicell Spheroid Model
 5.3 Thmour Angiogenesis. Capillary Sprout Formation and Growth
 5.4 Vascular Tumour Growth
 5.5 Discussion and Conclusions
 5.6 References
 6. Basic Models of TumorImmune System Interactions Identification, Analysis and Predictions
 6.1 Introduction
 6.2 Kinetics Models of Cellular Cytotoxic Reactions at the Effector Stage of Immune Response
 6.3 Regulatory Cells at the Effector Stage of the Cellular Immune Response
 6.4 Modeling of the Recognition Mechanisms of Thmor Cells by NKlike Cells
 6.5 Switch of Cytolytic Mechanisms: Effector Cells, Target Cells and Bispecific Regulating Molecules
 6.6 Propagation and Interaction of Tumor Specific Macromolecules in Multicellular Tumors
 6.7 Conclusion
 6.8 Acknowledgment
 6.9 References
 7. Tumor Heterogeneity and Growth Control
 7.1 Introduction
 7.2 The Goal
 7.3 The Plan
 7.4 The Foundation and Tools
 7.5 The Structure
 7.6 Conclusions
 7.7 References
 8. Biological Glossary
 Isbn
 9780817681197
 Label
 A Survey of Models for TumorImmune System Dynamics
 Title
 A Survey of Models for TumorImmune System Dynamics
 Statement of responsibility
 edited by John A. Adam, Nicola Bellomo
 Language
 eng
 Summary
 Mathematical Modeling and Immunology An enormous amount of human effort and economic resources has been directed in this century to the fight against cancer. The purpose, of course, has been to find strategies to overcome this hard, challenging and seemingly endless struggle. We can readily imagine that even greater efforts will be required in the next century. The hope is that ultimately humanity will be successful; success will have been achieved when it is possible to activate and control the immune system in its competition against neoplastic cells. Dealing with the abovementioned problem requires the fullest pos sible cooperation among scientists working in different fields: biology, im munology, medicine, physics and, we believe, mathematics. Certainly, bi ologists and immunologists will make the greatest contribution to the re search. However, it is now increasingly recognized that mathematics and computer science may well able to make major contributions to such prob lems. We cannot expect mathematicians alone to solve fundamental prob lems in immunology and (in particular) cancer research, but valuable sup port, however modest, can be provided by mathematicians to the research aspirations of biologists and immunologists working in this field
 Cataloging source
 AU@
 http://library.link/vocab/creatorName
 Adam, John A
 Dewey number
 510
 Index
 no index present
 LC call number
 QA1939
 Literary form
 non fiction
 Nature of contents
 dictionaries
 http://library.link/vocab/relatedWorkOrContributorName
 Bellomo, N
 Series statement
 Modeling and Simulation in Science, Engineering, & Technology,
 http://library.link/vocab/subjectName

 Mathematics
 Mathematics
 Label
 A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource)
 Antecedent source
 file reproduced from original
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 mixed
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 1. A Brief History of Immunologic Thinking: Is it Time for Yin and Yang?  1.1 Koch's Postulates  1.2 Aristotle's Laws of Logical Argument  1.3 Antigens and TCell Responses  1.4 Thinking About the Immune System  1.5 Fuzzy TCell Model  1.6 Acknowledgment  2. General Aspects of Modeling Tumor Growth and Immune Response  2.1 Introduction  2.2 What is a Mathematical Model?  2.3 Introduction to Deterministic Tumor (or Spheroid) Growth Models  2.4 A PredatorPrey Approach  2.5 A Model of Tumor Cell/Immune System Interaction  2.6 Models, Metaphors and Similes: Some Alternative Paradigms  2.7 References  2.8 Appendices  3. Mathematical Modeling of Tumor Growth Kinetics  3.1 Introduction  3.2 Tumor Growth as a Dynamical System  3.3 The Gompertz Model  3.4 The Logistic Model  3.5 Models of von Bertalanffy  3.6 Tumor Growth Modeled by Specific Mechanisms  3.7 Mathematical Models and Measured Growth Curves  3.8 Concluding Outlooks  3.9 Acknowledgment  3.10 References  4. Tumor Immune System Interactions: The Kinetic Cellular Theory  4.1 Introduction  4.2 A Concise Guide to the Literature  4.3 Guidelines: From Observation to Simulation  4.4 Cell Population and Activity  4.5 Modeling Cell Interactions  4.6 Evolution Kinetic Equations  4.7 Experimental Activity  4.8 Simulation and Validation Problems  4.9 Remarks Addressed to Applied Mathematicians  4.10 Perspectives  4.11 References  5. From Mutation to Metastasis: The Mathematical Modelling of the Stages of Tumour Development  5.1 Introduction  5.2 Avascular Tumour Growth: The Multicell Spheroid Model  5.3 Thmour Angiogenesis. Capillary Sprout Formation and Growth  5.4 Vascular Tumour Growth  5.5 Discussion and Conclusions  5.6 References  6. Basic Models of TumorImmune System Interactions Identification, Analysis and Predictions  6.1 Introduction  6.2 Kinetics Models of Cellular Cytotoxic Reactions at the Effector Stage of Immune Response  6.3 Regulatory Cells at the Effector Stage of the Cellular Immune Response  6.4 Modeling of the Recognition Mechanisms of Thmor Cells by NKlike Cells  6.5 Switch of Cytolytic Mechanisms: Effector Cells, Target Cells and Bispecific Regulating Molecules  6.6 Propagation and Interaction of Tumor Specific Macromolecules in Multicellular Tumors  6.7 Conclusion  6.8 Acknowledgment  6.9 References  7. Tumor Heterogeneity and Growth Control  7.1 Introduction  7.2 The Goal  7.3 The Plan  7.4 The Foundation and Tools  7.5 The Structure  7.6 Conclusions  7.7 References  8. Biological Glossary
 Dimensions
 unknown
 Extent
 1 online resource (xvi, 344 pages).
 File format
 unknown
 Form of item
 online
 Isbn
 9780817681197
 Isbn Type
 (electronic bk.)
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9780817681197
 Quality assurance targets
 unknown
 Reformatting quality
 access
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)853266309
 (OCoLC)ocn853266309
 Label
 A Survey of Models for TumorImmune System Dynamics, edited by John A. Adam, Nicola Bellomo, (electronic resource)
 Antecedent source
 file reproduced from original
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 mixed
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 1. A Brief History of Immunologic Thinking: Is it Time for Yin and Yang?  1.1 Koch's Postulates  1.2 Aristotle's Laws of Logical Argument  1.3 Antigens and TCell Responses  1.4 Thinking About the Immune System  1.5 Fuzzy TCell Model  1.6 Acknowledgment  2. General Aspects of Modeling Tumor Growth and Immune Response  2.1 Introduction  2.2 What is a Mathematical Model?  2.3 Introduction to Deterministic Tumor (or Spheroid) Growth Models  2.4 A PredatorPrey Approach  2.5 A Model of Tumor Cell/Immune System Interaction  2.6 Models, Metaphors and Similes: Some Alternative Paradigms  2.7 References  2.8 Appendices  3. Mathematical Modeling of Tumor Growth Kinetics  3.1 Introduction  3.2 Tumor Growth as a Dynamical System  3.3 The Gompertz Model  3.4 The Logistic Model  3.5 Models of von Bertalanffy  3.6 Tumor Growth Modeled by Specific Mechanisms  3.7 Mathematical Models and Measured Growth Curves  3.8 Concluding Outlooks  3.9 Acknowledgment  3.10 References  4. Tumor Immune System Interactions: The Kinetic Cellular Theory  4.1 Introduction  4.2 A Concise Guide to the Literature  4.3 Guidelines: From Observation to Simulation  4.4 Cell Population and Activity  4.5 Modeling Cell Interactions  4.6 Evolution Kinetic Equations  4.7 Experimental Activity  4.8 Simulation and Validation Problems  4.9 Remarks Addressed to Applied Mathematicians  4.10 Perspectives  4.11 References  5. From Mutation to Metastasis: The Mathematical Modelling of the Stages of Tumour Development  5.1 Introduction  5.2 Avascular Tumour Growth: The Multicell Spheroid Model  5.3 Thmour Angiogenesis. Capillary Sprout Formation and Growth  5.4 Vascular Tumour Growth  5.5 Discussion and Conclusions  5.6 References  6. Basic Models of TumorImmune System Interactions Identification, Analysis and Predictions  6.1 Introduction  6.2 Kinetics Models of Cellular Cytotoxic Reactions at the Effector Stage of Immune Response  6.3 Regulatory Cells at the Effector Stage of the Cellular Immune Response  6.4 Modeling of the Recognition Mechanisms of Thmor Cells by NKlike Cells  6.5 Switch of Cytolytic Mechanisms: Effector Cells, Target Cells and Bispecific Regulating Molecules  6.6 Propagation and Interaction of Tumor Specific Macromolecules in Multicellular Tumors  6.7 Conclusion  6.8 Acknowledgment  6.9 References  7. Tumor Heterogeneity and Growth Control  7.1 Introduction  7.2 The Goal  7.3 The Plan  7.4 The Foundation and Tools  7.5 The Structure  7.6 Conclusions  7.7 References  8. Biological Glossary
 Dimensions
 unknown
 Extent
 1 online resource (xvi, 344 pages).
 File format
 unknown
 Form of item
 online
 Isbn
 9780817681197
 Isbn Type
 (electronic bk.)
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9780817681197
 Quality assurance targets
 unknown
 Reformatting quality
 access
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)853266309
 (OCoLC)ocn853266309
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