Mathematical and Computational Challenges in Population Biology and Ecosystems Science

Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhance...

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Bibliographic Details
Published inScience (American Association for the Advancement of Science) Vol. 275; no. 5298; pp. 334 - 343
Main Authors Levin, Simon A., Grenfell, Bryan, Hastings, Alan, Perelson, Alan S.
Format Journal Article
LanguageEnglish
Published United States American Society for the Advancement of Science 17.01.1997
American Association for the Advancement of Science
The American Association for the Advancement of Science
Subjects
Acquired Immunodeficiency Syndrome - virology
AIDS
Allergy and Immunology
Animals
Bioinformatics
Biological Evolution
Biology
Biotic communities
Climate
Communicable Diseases - transmission
Computational Biology
Computer Simulation
Disease models
Ecological genetics
Ecological modeling
Ecology
Ecosystems
Epidemiology
Evolution
Evolutionary genetics
Genetics
Humans
Immunology
Infections
Mathematical models
Mathematics
Modeling
Models, Biological
Molecular Biology
Population
Population biology
Statistical Analysis
Structural Analysis (Linguistics)
Structural Analysis (Science)
Systems Approach
Virology
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Summary:Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.
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ISSN:0036-8075
1095-9203
DOI:10.1126/science.275.5298.334