NDLI: Measures of degeneracy and redundancy in biological networks.

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Measures of degeneracy and redundancy in biological networks.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Tononi, G. Edelman, G. M. Sporns, O.
Description	Author Affiliation: Tononi G ( The Neurosciences Institute, 10640 John J. Hopkins Drive, San Diego, CA 92121, USA. tononi@nsi.edu);
Abstract	Degeneracy, the ability of elements that are structurally different to perform the same function, is a prominent property of many biological systems ranging from genes to neural networks to evolution itself. Because structurally different elements may produce different outputs in different contexts, degeneracy should be distinguished from redundancy, which occurs when the same function is performed by identical elements. However, because of ambiguities in the distinction between structure and function and because of the lack of a theoretical treatment, these two notions often are conflated. By using information theoretical concepts, we develop here functional measures of the degeneracy and redundancy of a system with respect to a set of outputs. These measures help to distinguish the concept of degeneracy from that of redundancy and make it operationally useful. Through computer simulations of neural systems differing in connectivity, we show that degeneracy is low both for systems in which each element affects the output independently and for redundant systems in which many elements can affect the output in a similar way but do not have independent effects. By contrast, degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects. We demonstrate that networks that have been selected for degeneracy have high values of complexity, a measure of the average mutual information between the subsets of a system. These measures promise to be useful in characterizing and understanding the functional robustness and adaptability of biological networks.
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	6
Volume Number	96
Language	English
Publisher	National Academy of Sciences
Publisher Date	1999-05-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Models, Biological Models, Theoretical Nerve Net Physiology Animals Research Support, Non-U.S. Gov't Multidisciplinary
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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