title: Experimental and mathematical approaches to modeling plant metabolic networks.
abstract: To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.
authors.affiliation: Institute of Biological Chemistry, M.J. Murdock Metabolomics Laboratory, Center for Integrated Biotechnology, Washington State University, PO Box 646340, Pullman, WA 99164-6340, USA.
authors.forename: Rigoberto
authors.initials: R
authors.lastname: Rios-Estepa
authors.forename: Bernd Markus
authors.initials: BM
authors.lastname: Lange
mesh_terms: D002244:Carbon / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D007553:Isotope Labeling; D007700:Kinetics; D008954:Models, Biological; D019906:Nuclear Magnetic Resonance, Biomolecular; D010944:Plants / Q000378:metabolism*; D012639:Seeds / Q000378:metabolism; D012984:Software; D013025:Glycine max / Q000196:embryology / Q000378:metabolism
chemical_list: D002244:Carbon