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Integrated population biology and modeling. Part A /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
North Holland is an imprint of Elsevier,
2018.
|
| Colección: | Handbook of statistics (Amsterdam, Netherlands) ;
v. 39. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Integrated Population Biology and Modeling, Part A; Copyright; Contents; Contributors; Preface; Section I: Cellular Population Dynamics; Chapter 1: Population Dynamics and Evolution of Cancer Cells; 1. Introduction; 2. Evolutionary Dynamics of Escape From Tissue Homeostasis; 2.1. Mathematical Models of Tissue Homeostasis; 2.2. Evolutionary Dynamics of Feedback Escape; 2.3. Feedback, Stem Cell Enrichment, and Drug Resistance; 3. Telomeres and the Evolutionary Potential of Cells; 3.1. Replicative Limits and Cellular Hierarchy; 3.2. Replicative Limits and Precancerous Mutations
- 3.3. Replicative Limits in a Growing Cell Population4. Dynamics of Therapy Responses and Resistance Evolution; 4.1. Dynamics Underlying Chemoprevention With Aspirin; 5. Conclusions; References; Chapter 2: Stochastic and Deterministic Modeling of Cell Migration; 1. Introduction; 2. Cell Motility; 2.1. Connecting Stochastic and Deterministic Models of Cell Movement; 2.1.1. On-Lattice Models; 2.1.1.1. Noninteracting Cells Undergoing Unbiased Movement; 2.1.1.2. Excluding Cells; 2.1.2. Off-Lattice Models; 2.1.2.1. Noninteracting Cells Undergoing Unbiased Movement; 2.1.2.2. Volume-Excluding Cells
- 2.2. Higher Dimensions2.3. Higher Order Closure Approximations; 3. Model Extensions; 3.1. Cell Proliferation; 3.2. Cell Interactions; 3.2.1. Chemotaxis; 3.2.2. Adhesion-Repulsion; 3.2.3. Pushing and Pulling; 3.3. Growing Domains; 3.4. Persistence of Motion; 4. Conclusion; References; Chapter 3: Data-Driven Mathematical Modeling of Microbial Community Dynamics; 1. Introduction; 2. Microbial Community Profiling; 3. Mechanistic Modeling Approach; 3.1. Derivation of Specific LV Systems; 3.1.1. Derivation of Logistic Equation; 3.1.2. Derivation of Competition Model
- 3.1.3. Derivation of Cooperative Model3.1.4. Prey-Predator Model; 3.2. Generalized Lotka-Volterra Equations; 3.2.1. Equivalent Formulation via Replicator Dynamics; 3.3. Data Fitting; 3.3.1. Basic Theory of Parameter Fitting; 3.3.2. Maximum Likelihood Estimation; 3.3.3. Least Square Method; 3.3.4. Bayesian Inference; 3.3.5. Markov Chain Monte-Carlo; 3.3.6. Metropolis-Hastings Method; 3.3.7. Gibbs Sampling Method; 3.3.8. Hamiltonian Monte-Carlo Method; 3.3.9. Example of Bayesian Inference via Hamilton Monte-Carlo; 4. Data-Driven Approach; 4.1. Attractor Reconstruction From Time-Series Data
- 4.1.1. Basic Theory4.1.2. Practical Extension: Estimation of Lags; 4.1.3. Practical Extension: Estimation of Embedding Dimension; 4.1.4. Causality Inference of Nonlinear Time Series; 4.1.5. Examples of Attractor Reconstruction; 4.1.6. Examples of Causality Inference for Artificially Generated Time-Series Data; 4.1.7. Examples of Causality Inference for Microbiome Time-Series Data; 5. Conclusion; Acknowledgments; References; Chapter 4: Reaction-Diffusion Kinetics in Growing Domains; 1. Introduction; 2. Diffusion on a Uniformly Growing Domain; 2.1. Langevin Equation; 2.2. Fokker-Planck Equation