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      Spatial self-organization resolves conflicts between individuality and collective migration

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          Abstract

          Collective behavior can spontaneously emerge when individuals follow common rules of interaction. However, the behavior of each individual differs due to existing genetic and non-genetic variation within the population. It remains unclear how this individuality is managed to achieve collective behavior. We quantify individuality in bands of clonal Escherichia coli cells that migrate collectively along a channel by following a self-generated gradient of attractant. We discover that despite substantial differences in individual chemotactic abilities, the cells are able to migrate as a coherent group by spontaneously sorting themselves within the moving band. This sorting mechanism ensures that differences between individual chemotactic abilities are compensated by differences in the local steepness of the traveling gradient each individual must navigate, and determines the minimum performance required to travel with the band. By resolving conflicts between individuality and collective migration, this mechanism enables populations to maintain advantageous diversity while on the move.

          Abstract

          How bacteria migrate collectively despite individual phenotypic variation is not understood. Here, the authors show that cells spontaneously sort themselves within moving bands such that variations in individual tumble bias, a determinant of gradient climbing speed, are compensated by the local gradient steepness experienced by individuals.

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          Most cited references53

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          Phenotypic diversity, population growth, and information in fluctuating environments.

          Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
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            Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking

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              Bistability, epigenetics, and bet-hedging in bacteria.

              Clonal populations of microbial cells often show a high degree of phenotypic variability under homogeneous conditions. Stochastic fluctuations in the cellular components that determine cellular states can cause two distinct subpopulations, a property called bistability. Phenotypic heterogeneity can be readily obtained by interlinking multiple gene regulatory pathways, effectively resulting in a genetic logic-AND gate. Although switching between states can occur within the cells' lifetime, cells can also pass their cellular state over to the next generation by a mechanism known as epigenetic inheritance and thus perpetuate the phenotypic state. Importantly, heterogeneous populations can demonstrate increased fitness compared with homogeneous populations. This suggests that microbial cells employ bet-hedging strategies to maximize survival. Here, we discuss the possible roles of interlinked bistable networks, epigenetic inheritance, and bet-hedging in bacteria.
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                Author and article information

                Contributors
                thierry.emonet@yale.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 June 2018
                5 June 2018
                2018
                : 9
                : 2177
                Affiliations
                [1 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Molecular, Cellular, and Developmental Biology, , Yale University, ; New Haven, CT 06520 USA
                [2 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Physics, , Yale University, ; New Haven, CT 06520 USA
                [3 ]ISNI 0000000119573309, GRID grid.9227.e, Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, , Chinese Academy of Sciences, ; Shenzhen, 518055 China
                [4 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Computer Science, , Yale University, ; New Haven, CT 06520 USA
                [5 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Biomedical Engineering, , Yale University, ; New Haven, CT 06520 USA
                [6 ]ISNI 0000000119573309, GRID grid.9227.e, Present Address: Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, , Chinese Academy of Sciences, ; Shenzhen, 518055 China
                [7 ]ISNI 0000 0000 8711 3200, GRID grid.257022.0, Present Address: Department of Molecular Biotechnology, Graduate School of Advanced Sciences of Matter, , Hiroshima University, Higashi-Hiroshima, ; Hiroshima, 739-8530 Japan
                [8 ]ISNI 0000 0001 2168 0066, GRID grid.131063.6, Present Address: Department of Physics, , University of Notre Dame, ; Notre Dame, IN 46556 USA
                Author information
                http://orcid.org/0000-0003-3657-8296
                http://orcid.org/0000-0002-0495-8086
                http://orcid.org/0000-0002-6746-6564
                Article
                4539
                10.1038/s41467-018-04539-4
                5988668
                29872053
                7c57a1c9-10cc-45cb-8966-ca4c2ae4a3aa
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 July 2017
                : 3 May 2018
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