Self-organization in Biological Systems
Scott Camazine
Sep 2003 · Princeton University Press
Ebook
538
Pages
About this ebook
The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns--phenomena that have fascinated naturalists for centuries--a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world.
Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems?
Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology--a field of study at the forefront of life sciences research.
Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems?
Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology--a field of study at the forefront of life sciences research.
About the author
Scott Camazine is the author of The Naturalist's Year and Velvet Mites and Silken Webs. Jean-Louis Deneubourg is Research Fellow at the Belgian Fund for Scientific Research and at the Centre for Non-Linear Phenomena and Complex Systems at the Université Libre de Bruxelles, Belgium, where he is also Professor of Behavioral Ecology. Nigel R. Frank s is Professor of Animal Behavior and Ecology at the University of Bristol and the coauthor of The Social Evolution of Ants (Princeton). James Sneyd is Associate Professor of Mathematics at Massey University, New Zealand and the coauthor of Mathematical Physiology. Guy Theraulaz is Research Fellow at the National Center for Scientific Research in Toulouse, France, and at Paul Sabatier University. Eric Bonabeau is Chief Scientist at EuroBios in Paris, France. Bonabeau and Theraulaz are coauthors of Swarm Intelligence: From Natural to Artificial Systems.
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