Higher Plant Modelling for Bio-regenerative Life Support Including Metabolic Pathways Description

To build a model of higher plant growth in a closed greenhouse, it is necessary to describe physical constraints, physiological reactions, their interactions and their responses to the surroundings knowing that a robust modelling of plant growth must include principles of elements conservation. The major goal of plant growth modelling is to predict crop yield (biomass production), plant composition, O 2 production and CO2 consumption as function of process variables such as light energy availability, gas and liquid flow rates, etc. The targeted model will have many sub-models since it should also take the compartmentalized structure of plants into consideration and must include metabolic descriptions of the different parts of the plant. Photosynthesis takes place in leaves, while the other cells are devoted to respiration, storage of carbohydrates, up take of minerals. This leads to a model with different metabolic behaviours for each of the compartments and exchanges between the different parts. The model must have combined details of metabolic pathways of different parts of the plant, cell growth, dissolved component transport and mass balances. Here, we propose a metabolic flux computation method accounting for reactions stoichiometry and chemical energy conservation obtained from metabolic pathways description of the core metabolism of leaves, roots and stems. The system of equations is written in the form of a matrix which uses mass balance techniques and the assumption of metabolic steady state for the formulation of linear constraints. These constraints, together with measurements of extracellular uptake and excretion rates, assist in calculating consistent intracellular net reaction rates and consequently , ,, in the determination of flux distributions in the metabolic networks. Conversely, this stoichiometric model predicts growth and by-product productions as responses to process variables. In addition, sensitivity analysis of the parameters and the development of the general approaches in the reduction of model dimension are included in the model formulation.