Figures
Computational complementation.
Autoregulation of nodulation (AON) is a long-distance, shoot-root signalling system for regulating nodule formation in legume plants. This visualisation, taken from a computational complementation experiment, demonstrates the possible allocation of an unidentified signal for inhibition of nodulation in soybean root. In this approach, an empirical model of a loss-of-function (non-AON) mutant is complemented with hypothetical AON mechanisms. If the resulting nodulation phenotype matches the wild-type plant, the hypotheses would be supported as reasonable. The first application of computational complementation predicted that soybean cotyledons participate in AON, which was subsequently confirmed by a real-plant experiment (see Han et al., doi:10.1371/journal.pcbi.1000685).
Image Credit: Liqi Han (University of Queensland).
Citation: (2010) PLoS Computational Biology Issue Image | Vol. 6(2) February 2010. PLoS Comput Biol 6(2): ev06.i02. https://doi.org/10.1371/image.pcbi.v06.i02
Published: February 26, 2010
Copyright: © 2010 Han et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Autoregulation of nodulation (AON) is a long-distance, shoot-root signalling system for regulating nodule formation in legume plants. This visualisation, taken from a computational complementation experiment, demonstrates the possible allocation of an unidentified signal for inhibition of nodulation in soybean root. In this approach, an empirical model of a loss-of-function (non-AON) mutant is complemented with hypothetical AON mechanisms. If the resulting nodulation phenotype matches the wild-type plant, the hypotheses would be supported as reasonable. The first application of computational complementation predicted that soybean cotyledons participate in AON, which was subsequently confirmed by a real-plant experiment (see Han et al., doi:10.1371/journal.pcbi.1000685).
Image Credit: Liqi Han (University of Queensland).