Computational complementation: a modelling approach to study signalling mechanisms during legume autoregulation of nodulation

Autoregulation of nodulation (AON) is a long-distance signalling regulatory system maintaining the balance of symbiotic nodulation in legume plants. However, the intricacy of internal signalling and absence of flux and biochemical data, are a bottleneck for investigation of AON. To address this, a new computational modelling approach called ‘‘Computational Complementation’’ has been developed. The main idea is to use functional-structural modelling to complement the deficiency of an empirical model of a loss-of-function (non-AON) mutant with hypothetical AON mechanisms. If computational complementation demonstrates a phenotype similar to the wild-type plant, the signalling hypothesis would be suggested as ‘‘reasonable’’. Our initial case for application of this approach was to test whether or not wild-type soybean cotyledons provide the shoot-derived inhibitor (SDI) to regulate nodule progression. We predicted by computational complementation that the cotyledon is part of the shoot in terms of AON and that it produces the SDI signal, a result that was confirmed by reciprocal epicotyl-and-hypocotyl grafting in a real-plant experiment. This application demonstrates the feasibility of computational complementation and shows its usefulness for applications where real-plant experimentation is either difficult or impossible

Carbon allocation in fruit trees: from theory to modelling

International audienceCarbon allocation within a plant depends on complex rules linking source organs (mainly shoots) and sink organs (mainly roots and fruits). The complexity of these rules comes from both regulations and interactions between various plant processes involving carbon. This paper presents these regulations and interactions, and analyses how agricultural management can influence them. Ecophysiological models of carbon production and allocation are good tools for such analyses. The fundamental bases of these models are first presented, focusing on their underlying processes and concepts. Different approaches are used for modelling carbon economy. They are classified as empirical, teleonomic, driven by source–sink relationships, or based on transport and chemical/biochemical conversion concepts. These four approaches are presented with a particular emphasis on the regulations and interactions between organs and between processes. The role of plant architecture in carbon partitioning is also discussed and the interest of coupling plant architecture models with carbon allocatio

Insect – Tree Interactions in Thaumetopoea pityocampa

The pine processionary moth is, by far, the most important insect defoliator of pine forests in Southern Europe and North Africa, both in terms of its temporal occurrence, geographic range and socioeconomic impact. Monitoring and pest management actions are therefore required on a regular basis, to ensure the detection, evaluation and mitigation of potential risks to forest and public health. However, we still lack some of the basic knowledge required for relevant analyses of the risk posed by the pine processionary moth. Pest risk is defined as a combination of three components: (1) pest occurrence, which depends on the spatiotemporal dynamics of populations; (2) plant vulnerability to the pest, resulting in a certain amount of damage; and (3) the socioeconomic impact of damage, depending on the potential value of the plants damaged (Jactel et al. 2012). The population dynamics of the processionary moth has been extensively studied, in particular within the context of climate change (see Battisti et al. 2014, Chap. 2, this volume). Several studies have recently addressed the question of tree and forest vulnerability to pine processionary attacks but a comprehensive review of evidence was missing. This is the first objective of this chapter. In particular we were interested in a better understanding of the ecological mechanisms responsible for the host tree selection, at both the species and individual tree levels. In a second part we show that pine susceptibility to the pine processionary moth could be reduced by improving forest diversity at different spatial scales. In the last part of this chapter we provide quantitative estimate of the growth losses caused by defoliations of the pine processionary moth. Altogether this information paves the way for quantitative risk analyses on pine processionary moth infestations based on forest growth models