TY - GEN AB - To prevent epidemics, insect societies have evolved collective disease defences that are highly effective at curing exposed individuals and limiting disease transmission to healthy group members. Grooming is an important sanitary behaviour—either performed towards oneself (self-grooming) or towards others (allogrooming)—to remove infectious agents from the body surface of exposed individuals, but at the risk of disease contraction by the groomer. We use garden ants (Lasius neglectus) and the fungal pathogen Metarhizium as a model system to study how pathogen presence affects self-grooming and allogrooming between exposed and healthy individuals. We develop an epidemiological SIS model to explore how experimentally observed grooming patterns affect disease spread within the colony, thereby providing a direct link between the expression and direction of sanitary behaviours, and their effects on colony-level epidemiology. We find that fungus-exposed ants increase self-grooming, while simultaneously decreasing allogrooming. This behavioural modulation seems universally adaptive and is predicted to contain disease spread in a great variety of host–pathogen systems. In contrast, allogrooming directed towards pathogen-exposed individuals might both increase and decrease disease risk. Our model reveals that the effect of allogrooming depends on the balance between pathogen infectiousness and efficiency of social host defences, which are likely to vary across host–pathogen systems. AU - Theis, Fabian AU - Ugelvig, Line V AU - Marr, Carsten AU - Cremer, Sylvia ID - 9721 TI - Data from: Opposing effects of allogrooming on disease transmission in ant societies ER - TY - GEN AU - Friedlander, Tamar AU - Mayo, Avraham E. AU - Tlusty, Tsvi AU - Alon, Uri ID - 9718 TI - Supporting information text ER - TY - JOUR AB - We present a software platform for reconstructing and analyzing the growth of a plant root system from a time-series of 3D voxelized shapes. It aligns the shapes with each other, constructs a geometric graph representation together with the function that records the time of growth, and organizes the branches into a hierarchy that reflects the order of creation. The software includes the automatic computation of structural and dynamic traits for each root in the system enabling the quantification of growth on fine-scale. These are important advances in plant phenotyping with applications to the study of genetic and environmental influences on growth. AU - Symonova, Olga AU - Topp, Christopher AU - Edelsbrunner, Herbert ID - 1793 IS - 6 JF - PLoS One TI - DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots VL - 10 ER - TY - GEN AU - Symonova, Olga AU - Topp, Christopher AU - Edelsbrunner, Herbert ID - 9737 TI - Root traits computed by DynamicRoots for the maize root shown in fig 2 ER - TY - JOUR AB - Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved. AU - Friedlander, Tamar AU - Mayo, Avraham AU - Tlusty, Tsvi AU - Alon, Uri ID - 1827 IS - 3 JF - PLoS Computational Biology TI - Evolution of bow-tie architectures in biology VL - 11 ER -