Abstract
BackgroundDuring the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.ResultsThe thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.ConclusionsThe successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.
Highlights
During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy
Among the available modelling approaches are in silico based analyses, such as elementary flux mode analysis [20, 21] and extreme pathway analysis [22, 23], as well as analyses, which rely on experimental data to deliver necessary constraints, such as 13C-metabolic flux analysis (13C-MFA) [24,25,26], highthroughput isotope based metabolic screening [27], flux balance analysis [16] and metabolic control analysis [28]
Metabolic network construction The metabolic network used in this study comprises the central carbon metabolism of A. thaliana leaves
Summary
We face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. In silico simulation appears interesting due to its high speed, given e.g. the relatively long time of experiments with growing plants
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