Suchergebnisse

Background: Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings: In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 μmol m-2 s -1. Conclusions: Our results demonstrate that artificial intelligence models are not only successful in identifying complex nonlinear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. © 2014 Gago et al. ; This work was supported by The Regional Government Xunta de Galicia (PGIDIT02BTF30102PR) and Spanish MEC (AGL 2003-05877) to PPG. ML (PR2010-0460) and PPG (PR2010-0357) thank the Spanish Ministry of Education for their financial support during their sabbatical year at Faculty of Science, University of Utrecht, Netherlands ; Peer Reviewed