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Biomat 2012 - international symposium on mathematical and computational biology.
This is a book of a series on interdisciplinary topics of the Biological and Mathematical Sciences. The chapters correspond to selected papers on special research themes, which were presented at BIOMAT 2012 International Symposium on Mathematical and Computational Biology, in Tempe, Arizona, USA, No...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
World Scientific,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Ma 4500 | ||
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| 100 | 1 | |a Mondaini, Rubem P. | |
| 245 | 1 | 0 | |a Biomat 2012 - international symposium on mathematical and computational biology. |
| 260 | |b World Scientific, |c 2013. | ||
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| 520 | |a This is a book of a series on interdisciplinary topics of the Biological and Mathematical Sciences. The chapters correspond to selected papers on special research themes, which were presented at BIOMAT 2012 International Symposium on Mathematical and Computational Biology, in Tempe, Arizona, USA, November 6-10. This book contains state-of-the art articles on special research topics on mathematical biology, biological physics and mathematical modeling of biosystems; comprehensive reviews on interdisciplinary areas written by prominent leaders of scientific research groups. The treatment is both. | ||
| 505 | 0 | |a Preface; Editorial Board of the BIOMAT Consortium; Professor C.E.M. Pearce -- In Memoriam; Mathematical Epidemiology; Compartmental Age of Infection Epidemic Models Fred Brauer; 1. Epidemic models with homogeneous mixing; 1.1. The simple Kermack-McKendrick model; 1.2. Models with disease deaths; 1.3. More complicated epidemic models; 2. The age of infection epidemic model; 2.1. The initial exponential growth rate; 3. Heterogeneous mixing age of infection models; 3.1. The final size relations; 3.2. The initial exponential growth rate; 4. Different models for the same epidemic. | |
| 505 | 8 | |a Quantifying the Risk of Mosquito-Borne Infections Basing on the Equilibrium Prevalence in Humans Marcos Amaku, Francisco A.B. Coutinho, Eduardo Massad1. Introduction; 2. The Model; 3. Estimating Risks; 4. Discussion; Acknowledgments; Conflicts of Interest; References; Seasonal Fluctuation in Tsetse Fly Populations and Human African Trypanosomiasis: A Mathematical Model T. Madsen, D.I. Wallace, N. Zupan; 1. Introduction; 2. Insect population submodel; 2.1. Insect Model Equations; 2.2. Explanation of Equations; 2.3. Temperature Model; 3. Analysis of model. | |
| 505 | 8 | |a 3.1. Instability of the model with constant temperature D3.2. Sufficient insect death leads to stability; 3.3. Variable temperature as a switched system; 4. Numerical results of insect submodel; 4.1. Rogers' model revisited; 5. Sensitivity of the model; 6. Summary of results; References; Modelling Physiological Disorders; A Mathematical Model for the Immunotherapy of Advanced Prostate Cancer Travis Portz, Yang Kuang; 1. Introduction; 2. The Model; 3. Numerical Simulations; 4. Mathematical Analysis; 5. Discussion; Acknowledgements; References. | |
| 505 | 8 | |a Seizure Manifold of the Epileptic Brain: A State Space Reconstruction Approach Mujahid N. Syed, Pando G. Georgiev, Panos M. Pardalos1. Introduction; 2. Review; 2.1. Embedding; 3. Methodology; 3.1. Preprocessing; Filtering Noise; Identifying Stationarity; Identifying Determinism; 3.2. Manifold Generation; Time Delay Embedding; Embedding Dimension; Delay Time; 3.3. Measures of DDS; Fractal Dimension; Lyapunov Exponents; Kolmogorov Entropy; 3.4. Surrogate Tests; Surrogate Data Test 1; Surrogate Data Test 2; Surrogate Data Test 3; 3.5. Low Dimensional Phase Portraits; 4. Seizure Manifold. | |
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