title: Metabolic network properties help assign weights to elementary modes to understand physiological flux distributions.
abstract: BACKGROUND
  Elementary modes (EMs) analysis has been well established. The existing methodologies for assigning weights to EMs cannot be directly applied for large-scale metabolic networks, since the tremendous number of modes would make the computation a time-consuming or even an impossible mission. Therefore, developing more efficient methods to deal with large set of EMs is urgent.
  
  
  RESULTS
  We develop a method to evaluate the performance of employing a subset of the elementary modes to reconstruct a real flux distribution by using the relative error between the real flux vector and the reconstructed one as an indicator. We have found a power function relationship between the decrease of relative error and the increase of the number of the selecting EMs, and a logarithmic relationship between the increases of the number of non-zero weighted EMs and that of the number of the selecting EMs. Our discoveries show that it is possible to reconstruct a given flux distribution by a selected subset of EMs from a large metabolic network and furthermore, they help us identify the 'governing modes' to represent the cellular metabolism for such a condition.
authors.affiliation: Metabolic Engineering Laboratory, Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, People's Republic of China. Wangqingzhao@eyou.com
authors.forename: Qingzhao
authors.initials: Q
authors.lastname: Wang
authors.forename: Yudi
authors.initials: Y
authors.lastname: Yang
authors.forename: Hongwu
authors.initials: H
authors.lastname: Ma
authors.forename: Xueming
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome