Horticulture Lighting – systems biology

(September 2012 – August 2016)

Project description

Light is essential for the plant growth and development. However, the traditional artificial light applied in plant factory and plant tissue propagation cannot replace the natural solar light as to the light intensity and quality. The goal of this project is to develop a novel approach for illumination in horticulture and associated tool set to improve crop yield and quality. To reach this aim, this project concentrates on three aspects of light requirement of plant growth and development: (1) identification of light quality increasing plant disease resistances; (2) improvement of the phytochemicals which are beneficial for human health; and (3) building computational models for several horticultural crops of commercial importance under different lighting conditions. Conventional light source is replaced by light emitting diode (LED) due to their energy-saving and monochromatic light property. Based on the comprehensive evaluation of the plant growth performance, disease resistance and nutritional value, an optimized lighting facility, setting and illumination management can be designed. By integrating the data archived from the literature, online databases and the above experiments, computational models are established in the studied crops. These models are used to predict the plant performance under certain lighting conditions to help the illumination management design. Improvements in artificial lights in greenhouse can reduce the energy cost, explore the yield potent of the crop, control the disease in an environmental friendly way and increase the content of metabolite beneficial for human health.

Project Participants

Main results (publications, patents)

Yuan, H., Cheung, C.Y. Maurice, Poolman, M.G., Hilbers, P.A.J. & van Riel, N.A.W. (2016). A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism. Plant Journal, 85(2), 289-304

Yuan, H., Cheung, C.Y. Maurice, Hilbers, P.A.J. & van Riel, N.A.W. (2016). Flux balance analysis of plant metabolism : the effect of biomass composition and model structure on model predictions. Frontiers in Plant Science, 7:537

Yuan, H. (2016). Genome-scale metabolic modelling of Arabidopsis and tomato. PhD Thesis: Technische Universiteit Eindhoven, http://repository.tue.nl/fbdb4271-6184-48d6-b0d6-7d49c67ed715


The team

Cellular metabolism is a complex process which encompasses all biochemical reactions taking place within a cell. These biochemical reactions are organized into numerous metabolic pathways which are often interconnected, shaping a dynamic circuitry commonly referred as metabolic network. In living organisms, the metabolic network plays an important role in the cell survival. Studying the regulation of the metabolic network will eventually contribute to understanding cellular mechanisms. In this regard, mathematical and computational approaches, in particular, metabolic network modelling combined with flux balance analysis (FBA), and related methods, are increasingly recognized as a powerful tool to systematically study the organization of metabolic networks. In the Horticulture Lighting – systems biology project we used the constraint-based modelling framework to investigate the functions of plant biological processes and the effects of environmental perturbations, such as availability of water and light.

‘Google maps’ of metabolism:

Utilization of mevalonate (MVA) pathway instead of MEP pathway under drought stress. Published as: Yuan et al, Plant J. 2016 Jan;85(2):289-304

A predictive model has been developed of the metabolic network of tomato:

Model predicts changes in energy metabolism under drought stress:

PhD thesis Huili Yuan: