title: Green pathways: Metabolic network analysis of plant systems.
abstract: Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.
authors.affiliation: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123 Saarbrücken, Germany. Electronic address: lisa.dersch@uni-saarland.de.
authors.forename: Lisa Maria
authors.initials: LM
authors.lastname: Dersch
authors.affiliation: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123 Saarbrücken, Germany. Electronic address: veronique.beckers@uni-saarland.de.
authors.forename: Veronique
authors.initials: V
authors.lastname: Beckers
authors.affiliation: Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123 Saarbrücken, Germany. Electronic address: christoph.wittmann@uni-saarland.de.
authors.forename: Christoph
authors.initials: C
authors.lastname: Wittmann
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D055772:Green Chemistry Technology / Q000379:methods; D064688:Metabolic Flux Analysis / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological; D010940:Plant Proteins / Q000378:metabolism*; D010944:Plants / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D010940:Plant Proteins
keywords: (13)C metabolic flux analysis; Arabidopsis; Compartment; Elementary flux mode analysis; Flux balance analysis; GC/MS; Isotopically non-stationary; NMR; Plant network