naa a22 4500 528786695 20180924065501.0 cr unu---uuuuu 180924e20070214xx s 000 0 eng 10.3929/ethz-b-000004359 doi 10.1371/journal.pone.0000217 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/4359 Quantifying Organismal Complexity using a Population Genetic Approach [Elektronische Daten] [Olivier Tenaillon, Olin K. Silander, Jean-Philippe Uzan, Lin Chao] PLoS ONE Open access ethresearch Background Various definitions of biological complexity have been proposed: the number of genes, cell types, or metabolic processes within an organism. As knowledge of biological systems has increased, it has become apparent that these metrics are often incongruent. Methodology Here we propose an alternative complexity metric based on the number of genetically uncorrelated phenotypic traits contributing to an organism's fitness. This metric, phenotypic complexity, is more objective than previous suggestions, as complexity is measured from a fundamental biological perspective, that of natural selection. We utilize a model linking the equilibrium fitness (drift load) of a population to phenotypic complexity. We then use results from viral evolution experiments to compare the phenotypic complexities of two viruses, the bacteriophage X174 and vesicular stomatitis virus, and to illustrate the consistency of our approach and its applicability. Conclusions/Significance Because Darwinian evolution through natural selection is the fundamental element unifying all biological organisms, we propose that our metric of complexity is potentially a more relevant metric than others, based on the count of artificially defined set of objects. Creative Commons Attribution 2.0 Generic http://creativecommons.org/licenses/by/2.0 ethresearch Tenaillon Olivier joint author Silander Olin K. joint author Uzan Jean-Philippe joint author Chao Lin joint author PLoS ONE San Francisco, CA, USA : Public Library of Science 2 (2), p. e217 1932-6203 http://hdl.handle.net/20.500.11850/4359 text/html WWW-Backlink auf das Repository (Open access) 1 Journal Article ethresearch ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/4359 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 700 1- Tenaillon Olivier joint author ETHRESEARCH 700 1- Silander Olin K. joint author ETHRESEARCH 700 1- Uzan Jean-Philippe joint author ETHRESEARCH 700 1- Chao Lin joint author ETHRESEARCH 773 0- PLoS ONE San Francisco, CA, USA : Public Library of Science 2 (2), p. e217 1932-6203 BK010053 XK010053 XK010000 ETHRESEARCH ETHRESEARCH ETHRESEARCH Journal Article Open access