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Background and Aims Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype–phenotype model, we present here a three-dimensional functional–structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. Methods The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Key Results Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. Conclusions We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed. [ABSTRACT FROM PUBLISHER]

• A multiscaled ecophysiological model of barley (Hordeum vulgare) development is presented here.• The model is based on the new formalism of relational growth grammars (RGG), an extension of L-systems, and implemented using the new modelling language XL. It is executable in the interactive modelling platform GroIMP.• The model consists of a set of morphogenetic rules, combined with a metabolic regulatory network, which simulates the biosynthesis of gibberellic acid (GA1). GA1 and two of its metabolic precursors are transported along the developing simulated structure. Local concentrations of GA1 determine internode elongation. Furthermore, virtual barley individuals are chosen interactively from a population, based on genotype, and (sexual or asexual) reproduction is simulated. Genotype and phenotype of the population are visualized. Seven Mendelian genes have been implemented in the model so far; some of these directly influence the GA-regulation network.• The model exemplifies and validates the new formalism and modelling language. RGG have the capability to represent genetic, metabolic and morphological aspects of plant development and reproduction, all within the same framework.New Phytologist(2005)doi: 10.1111/j.1469-8137.2005.01324.x© New Phytologist(2005) [ABSTRACT FROM AUTHOR]

Abstract: In simulation models of plant development, physiological processes taking place in plants are typically described in terms of ODEs (Ordinary Differential Equations). On the one hand, those processes drive the development of the plant structure and on the other hand, the developed structure again influences these processes (e.g., photosynthesis, hormone synthesis and transport, and allocation of carbon, nitrogen, etc.). To study this dependence, simulation models, termed functional–structural plant models (FSPMs), are developed. Such models usually operate at the organ scale, considering the topology and the geometry of organs, while being validated at the scale of the plant individual. The open source modelling platform GroIMP was designed for the purpose of creating FSPMs. In GroIMP, the structure of a plant is described by the eXtended L-system language (XL) which is an extension of the Java programming language and works on a general graph structure. It is general enough to be used for many biological problems that can be described by graphs. Until now, to specify and solve ODEs, Java code had to be used and there was no general solution for doing this easily and conveniently in XL. Here we propose an extension to the XL language that allows the user to easily specify ODEs in terms of rule applications. Furthermore, their specification is separated from the numerical solution, with the possibility to choose between different integration methods. The new framework is illustrated with examples of auxin transport in Arabidopsis and gibberellic acid signal transduction in barley and compared with the conventional approach in FSPMs (Euler method). We show that besides the user-friendly specification of ODEs within rules by using a special operator, the results are computed faster, are more stable and accurate. The new framework is also compared with the mathematical formalism of differential L-systems (dL-systems). [Copyright &y& Elsevier]