|
|
|
|
| LEADER |
05088nmm a2200397 u 4500 |
| 001 |
EB000892649 |
| 003 |
EBX01000000000000000689769 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
140801 ||| eng |
| 020 |
|
|
|a 9783319056159
|
| 100 |
1 |
|
|a Hannon, Bruce
|
| 245 |
0 |
0 |
|a Modeling Dynamic Biological Systems
|h Elektronische Ressource
|c by Bruce Hannon, Matthias Ruth
|
| 250 |
|
|
|a 2nd ed. 2014
|
| 260 |
|
|
|a Cham
|b Springer International Publishing
|c 2014, 2014
|
| 300 |
|
|
|a XVI, 434 p. 298 illus., 280 illus. in color
|b online resource
|
| 505 |
0 |
|
|a I. INTRODUCTION -- 1. Modeling Dynamic Biological Systems -- 2. Exploring Dynamic Biological Systems -- 3. Risky Population -- 4. Steady State, Oscillation and Chaos in Population Dynamics -- 5. Spatial Dynamics.- II. PHYSICAL AND BIOCHEMICAL MODELS.- 6. Law of Mass Action -- 7. Catalyzed Product -- 8. Two-Stage Nutrient Uptake -- 9. Iodine Compartment -- 10. The Brusselator -- 11. Signal Transmission -- III. Genetic Models -- 12. Mating and Mutation of Alleles -- 13. Artificial Worms -- 14. Langur Infanticide and Long-term Matriline Fitness -- IV. MODELS OF ORGANISM -- 15. Odor Sensing -- 16. Stochastic Resonance -- 17. Heart Beat -- 18. Bat Thermo-Regulation -- 19. The Optimum Plant -- 20. Soybean Plant Growth -- 21. Infectious Diseases -- VI. SINGLE POPULATION MODELS -- 22. Adaptive Population Control -- 23. Roan Herds -- 24. Population Dynamics of Voles -- 25. Lemming Population Dynamics -- 26. Multi-Stage Insect Models -- 27. Two Age-Class Parasites -- 28. Monkey Travels --
|
| 505 |
0 |
|
|a 29. Biosynchronicity -- VII. MULTIPLE POPULATION MODELS -- 30. Plant Microbe Interaction -- 31. Wildebeest -- 32. Nicholson-Bailey Host-Parasite Interaction -- 33. Diseased and Healthy Immigrating Insects -- 34. Two-Species Colonization Model -- 35. Herbivore-Algae Predator-Prey Dynamics -- 36. The Grass Carp -- 37. Recruitment and Trophic Dynamics of Gizzard Shad -- 38. Salamander Dispersal. 39. Quail Movement -- 40. Modeling Spatial Dynamics of Spatial Predator-Prey Interactions in a Changing -- VII. CATASTROPHE AND SELF-ORGANIZATION -- 41. Catastrophe -- 42. Spruce Budworm Dynamics -- 43. Game of Life -- 44. Daisyworld -- VIII. CONCLUSION --
|
| 505 |
0 |
|
|a 45. Building a Modeling Community.
|
| 653 |
|
|
|a Computers
|
| 653 |
|
|
|a Biochemistry, general
|
| 653 |
|
|
|a Biomathematics
|
| 653 |
|
|
|a Ecology
|
| 653 |
|
|
|a Mathematical and Computational Biology
|
| 653 |
|
|
|a Models and Principles
|
| 653 |
|
|
|a Biochemistry
|
| 653 |
|
|
|a Population
|
| 653 |
|
|
|a Ecology
|
| 653 |
|
|
|a Population Economics
|
| 700 |
1 |
|
|a Ruth, Matthias
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Modeling Dynamic Systems
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-05615-9?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 577
|
| 520 |
|
|
|a Many biologists and ecologists have developed models that find widespread use in theoretical investigations and in applications to organism behavior, disease control, population and metapopulation theory, ecosystem dynamics, and environmental management. This book captures and extends the process of model development by concentrating on the dynamic aspects of these processes and by providing tools that virtually anyone with basic knowledge in the Life Sciences can use to develop meaningful dynamic models. Examples of the systems modeled in the book range from models of cell development, the beating heart, the growth and spread of insects, spatial competition and extinction, to the spread and control of epidemics, including the conditions for the development of chaos. Key Features · Easy-to-learn and easy-to-use software · Includes examples from many subdisciplines of biology, covering models of cells, organisms, populations, and metapopulations · No prior computer or programming experience required Key Benefits · Learn how to develop modeling skills and system thinking on your own rather than use models developed by others · Easily run models under alternative assumptions and investigate the implications of these assumptions for the dynamics of the biological system being modeled · Develop skills to assess the dynamics of biological systems
|