The shade avoidance syndrome (SAS) is one of the best-studied forms of plant phenotypic plasticity. The suite of SAS responses enables plants to accurately match their phenotype to the light conditions determined by neighbouring plants, especially the decrease in the ratio of red (R) and far red (FR) light intensity (R : FR) (Ballar e et al., 1990). In recent years, significant progress has been made in understanding the physiological and molecular regulation of SAS (among others reviewed in Casal, 2013; Gommers et al., 2013; Pierik & de Wit, 2014). In addition, several studies have shown that SAS is adaptive because inappropriate elongation resulting from inaccurate estimation of neighbour proximity or deficiency in the capacity to respond to neighbour presence is disadvantageous for fitness (Dudley & Schmitt, 1995, 1996; Weinig, 2000; Pierik et al., 2003; Weijschede et al., 2008; Keuskamp et al., 2010). However, it is difficult to assess the consequences of detailed physiological and molecular regulations of SAS for whole-plant and whole-vegetation performance. Furthermore, the wide variety of different cues involved in plant–plant interactions, including light quality and quantity (Ballar e et al., 1990; Smith, 2000), mechanical interaction (i.e. touch and wind shielding; Anten et al., 2005; de Wit et al., 2012) and various volatiles (Pierik et al., 2003; Kegge et al., 2013), poses questions about their relative importance for plant performance. Here, we argue that the consequences of physiological regulations and the complexity of natural systems can be addressed by using virtual plant simulation modelling in combination with experimental studies. So-called functional–structural plant (FSP) models have been applied in a broad range of research questions in the field of plant sciences (reviewed in Vos et al., 2010; DeJong et al., 2011; Guo et al., 2011; Prusinkiewicz & Runions, 2012) and simulate plant development over time in three dimensions (the principles are outlined in Box 1). These models can include responses to environmental conditions such as light (Fig. 1) and can be used to study how the interplay among physiology, architecture and environment scale from plant organ to wholeplant performance. FSP models can generate and test hypotheses about the influence of different environmental components on plant growth and development by including one component at a time. By comparing model predictions with naturally developed vegetation stands, the contribution of the different components of environmental factors to plant performance can be assessed. In this Letter, we outline the way in which FSP models can improve experimental designs and how data collected from these experiments can, in turn, improve the mechanistic description of regulation of shade avoidance in the model. Ultimately, this feedback process results in a modelling tool that can scale up from plant organ responses to whole-plant performance and address ecologically relevant questions such as the adaptive significance of variation in SAS.