Mathematical Immunology of Virus Infections 1st ed. 2018 [Hardback]

  • Formāts: Hardback, 245 pages, height x width: 235x155 mm, weight: 561 g, XV, 245 p., 1 Hardback
  • Izdošanas datums: 22-Jun-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319723162
  • ISBN-13: 9783319723167
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  • Formāts: Hardback, 245 pages, height x width: 235x155 mm, weight: 561 g, XV, 245 p., 1 Hardback
  • Izdošanas datums: 22-Jun-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319723162
  • ISBN-13: 9783319723167
Citas grāmatas par šo tēmu:
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. 

Recenzijas

"This book on the whole provides a nice guide for applied mathematicians who are interested in understanding immunology of virus infections and also for biologists who are seeking rigorous modeling methods for issues raised in immune systems." (Yilun Shang, zbMATH 1401.92002, 2019)

1 Principles of Virus-Host Interaction
1(14)
1.1 In Brief
1(3)
1.2 Virus Recognition and Immune Responses
4(6)
1.2.1 Pattern Recognition to Initiate Innate Immune Responses
6(1)
1.2.2 Viral Antigen Recognition by Adaptive Immune Responses
7(3)
1.3 Infection Fates with a Glimpse on the Real Complexity of Infection Immunology
10(5)
References
14(1)
2 Basic Principles of Building a Mathematical Model of Immune Response
15(20)
2.1 Systems Approach to Immunology
15(3)
2.1.1 Theories in Immunology
17(1)
2.2 A Mathematical Model
18(3)
2.2.1 Basic Issues
19(1)
2.2.2 Dynamics of Immune Responses
20(1)
2.3 Elementary Building Blocks for Models
21(14)
2.3.1 Ag-Ab Interaction
21(1)
2.3.2 Growth Phenomena
22(5)
2.3.3 Lymphocyte Proliferation
27(1)
2.3.4 Cell Death
28(2)
2.3.5 Cell Differentiation
30(1)
2.3.6 Tuning of the Response
30(2)
2.3.7 Cell Competition
32(1)
References
33(2)
3 Parameter Estimation and Model Selection
35(62)
3.1 General Modelling Issues
35(2)
3.2 Parameter Estimation
37(15)
3.2.1 Maximum Likelihood Approach
38(1)
3.2.2 Least-Squares Type Objective Functions
39(1)
3.2.3 Uncertainty Quantification
40(1)
3.2.4 Variance-Covariance Analysis
41(1)
3.2.5 Profile-likelihood-based Method
41(1)
3.2.6 Bootstrap Method
42(1)
3.2.7 Example of Computational Analysis of CFSE Proliferation Assay
42(10)
3.3 Regularization of Parameter Estimation
52(24)
3.3.1 Distributed Parameter Model
53(2)
3.3.2 Distributed Parameter Estimation
55(4)
3.3.3 Regularization of the Parameter Estimation
59(3)
3.3.4 Cell Growth Model with Asymmetry and Time Lags
62(2)
3.3.5 Division-Structured DDE Model
64(2)
3.3.6 Asymmetric Division and Label-Structured Delay hPDE Model
66(5)
3.3.7 dhPDE Model
71(5)
3.4 Model Ranking and Selection
76(21)
3.4.1 Accuracy and Parsimony
77(1)
3.4.2 Information-Theoretic Basis for Model Selection
77(1)
3.4.3 Akaike Criteria
78(4)
3.4.4 Rival Models for Virus-CTL Dynamics
82(2)
3.4.5 Information-Theoretic Model Evaluation
84(5)
3.4.6 Minimum Description Length
89(1)
3.4.7 Summary
90(1)
References
90(7)
4 Modelling of Experimental Infections
97(56)
4.1 Why Experimental Infections?
97(1)
4.2 The LCMV System: Gold Standard for Infection Biology
98(30)
4.2.1 Immunobiology of LCMV
98(3)
4.2.2 Basic Mathematical Model of LCMV Infection
101(5)
4.2.3 Viral Parameters: Impact on the Infection Phenotype
106(10)
4.2.4 Role of CD8+ T Cells: Protection, Exhaustion, Immunopathology
116(12)
4.3 Parameters Defining a Robust DC-Induced CTL Expansion
128(10)
4.3.1 The Experimental Model of LCMV gp33-Specific CTL Induction
129(1)
4.3.2 Mathematical Model for DC-Induced Systemic Dynamics of CTL Responses
130(8)
4.4 MHV Infection: How Robust Is the IFN Type I-Mediated Protection?
138(7)
4.4.1 Immunobiology of MHV Infection
138(2)
4.4.2 Setting up a Mathematical Model
140(1)
4.4.3 Parameter Estimates and Sensitivity Analysis
141(4)
4.5 Identifying a Feedback Regulating Proliferation and Differentiation of CD4+ T Cells
145(8)
References
148(5)
5 Modelling of Human Infections
153(42)
5.1 Outcome of Virus Infections as a `Numbers Game'
153(2)
5.2 Reference Curves: HIV and Memory T-Cell Decay Under HAART
155(2)
5.3 Chronic HBV Infection
157(10)
5.3.1 Deterministic Model of HBV Infection
158(1)
5.3.2 Sneaking Through Phenomenon
159(3)
5.3.3 Low-Level HBV Persistence
162(5)
5.4 Spontaneous Recovery from Chronic HBV Infection
167(12)
5.4.1 Stochastic Framework for Modelling HBV Infection
167(2)
5.4.2 Quantitative Spectrum of Chronic HBV Infection
169(1)
5.4.3 Numerical Methods
170(6)
5.4.4 Determinants of Spontaneous Recovery
176(3)
5.5 Pathogenesis of Chronic HBV Infection via Adjoint Equations Sensitivity Analysis
179(16)
5.5.1 Mathematical Model of Antiviral Immune Response
181(3)
5.5.2 Sensitivity of Functionals to Deviations of Parameters from `norms'
184(2)
5.5.3 Numerical Treatment
186(1)
5.5.4 Adjoint Equations for the Antiviral Immune Response Model
187(2)
5.5.5 HBV Infection: Chronic Versus Resolving Infection
189(4)
References
193(2)
6 Spatial Modelling Using Reaction-Diffusion Systems
195(26)
6.1 Reaction-Diffusion Equations for Immunology
195(7)
6.1.1 Spatial Models of Infection Development
195(3)
6.1.2 Existence and Stability of Waves
198(4)
6.2 Virus Spreading in Tissue
202(6)
6.2.1 The Model Without Time Delay
202(3)
6.2.2 The Model with Time Delay
205(2)
6.2.3 Full-Scale Viral Regulation of the Immune Response
207(1)
6.3 Spatial Model of Virus and Immune Cells Dynamics
208(2)
6.4 Predicting the Type I IFN Field in Lymph Nodes During a Cytopathic Virus Infection
210(11)
6.4.1 Reaction-Diffusion Model of IFN Dynamics
211(1)
6.4.2 3D Approximation of a Paradigmatic Lymph Node
212(3)
6.4.3 Numerical Results
215(3)
References
218(3)
7 Multi-scale and Integrative Modelling Approaches
221(22)
7.1 Multi-scale Models
221(1)
7.2 Multi-scale Approaches in Mathematical Immunology
221(7)
7.2.1 Equations of Cell Kinetics and Cell Dynamics
223(2)
7.2.2 Global Extracellular Regulation
225(1)
7.2.3 Local Extracellular Regulation
226(1)
7.2.4 Intracellular Regulation
227(1)
7.3 Hybrid Multi-scale Models
228(10)
7.3.1 Hybrid Models of Immune Response
229(6)
7.3.2 Numerical Experiments
235(3)
7.3.3 Infection Spreading in the Lymph Node
238(1)
7.4 Basis for Further Work
238(5)
References
240(3)
8 Current Challenges
243