title: In silico metabolic network analysis of Arabidopsis leaves.
abstract: 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. 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.
  
  
  
  The 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.
  
  
  
  The 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.
authors: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany.
authors: Veronique
authors: V
authors: Beckers
authors: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany.
authors: Lisa Maria
authors: LM
authors: Dersch
authors: Metanomics GmbH, Berlin, Germany.
authors: Katrin
authors: K
authors: Lotz
authors: Institute of Biochemical Engineering, Technical University Braunschweig, Braunschweig, Germany.
authors: Guido
authors: G
authors: Melzer
authors: Metanomics GmbH, Berlin, Germany.
authors: Oliver Ernst
authors: OE
authors: Bläsing
authors: Metanomics GmbH, Berlin, Germany.
authors: Regine
authors: R
authors: Fuchs
authors: Metanomics GmbH, Berlin, Germany.
authors: Thomas
authors: T
authors: Ehrhardt
authors: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany. christoph.wittmann@uni-saarland.de.
authors: Christoph
authors: 0000-0002-7952-985X
authors: C
authors: Wittmann
mesh_terms: D017360:Arabidopsis / Q000378:metabolism* / Q000528:radiation effects; D003198:Computer Simulation; D003624:Darkness; D004734:Energy Metabolism / Q000528:radiation effects; D053858:Metabolic Networks and Pathways / Q000528:radiation effects; D010084:Oxidation-Reduction; D010788:Photosynthesis / Q000528:radiation effects; D018515:Plant Leaves / Q000378:metabolism* / Q000528:radiation effects; D049490:Systems Biology
keywords: Arabidopsis; Day-night shift; Electron flow; Elementary flux modes; Fluxome; In silico; In vivo; Transcriptome