ИСТИНА

Accumulation of experimental data on the structure and functions of cell systems, as well as the development of computer technology induce the formation of "e-cell" models, which include processes of different hierarchical levels, such as gene expression, metabolic reactions, and the reaction of electron transfer in the respiratory and photosynthetic chains. Various mathematical methods are used for different hierarchical levels: differential equations, stoichiometric models, rule-based models etc. To combine different hierarchical levels one meets the problem of integration the processes with different characteristic times usually described by different kinds of models. In this work we consider the hierarchical model of the plant cell that combines the description of primary photosynthetic and metabolic processes. The sub-model of photosynthetic reaction is presented by sets of differential equations for concentrations of multi-enzyme complexes states. We describe metabolic paths by means of algebraic equations according to Flux Balance Analysis formalism. The model contains both the kinetic block of primary photosynthetic reactions and the flux balanced model of central metabolic pathways based on stoichiometry of metabolic reactions. In the model NADP reduction connects the photosynthetic reaction block with the metabolic one. The metabolic block is presented by glycolysis, Calvin-Benson Cycle and TCA Cycle and depicts the stationary distribution of central metabolic pathways. NADP stationary influx in Calvin-Benson Cycle is modified by the kinetic block of primary photosynthetic reactions that allows obtaining the series of metabolic flux distributions under different conditions. The hierarchical model was applied for description of evolution of the metabolic fluxes distribution in algae cells under mineral starvation. The combined hierarchical model thus allows us to see how fast photosynthetic and slow dark metabolic reactions are coupled and study the total response of different cell systems to stress factors. This work was supported by RFBR grant № 14-04-00326-a.