title: Comparison on extreme pathways reveals nature of different biological processes.
abstract: BACKGROUND
  Constraint-based reconstruction and analysis (COBRA) is used for modeling genome-scale metabolic networks (MNs). In a COBRA model, extreme pathways (ExPas) are the edges of its conical solution space, which is formed by all viable steady-state flux distributions. ExPa analysis has been successfully applied to MNs to reveal their phenotypic capabilities and properties. Recently, the COBRA framework has been extended to transcriptional regulatory networks (TRNs) and transcriptional and translational networks (TTNs), so efforts are needed to determine whether ExPa analysis is also effective on these two types of networks.
  
  
  RESULTS
  In this paper, the ExPas resulting from the COBRA models of E.coli's MN, TRN and TTN were horizontally compared from 5 aspects: (1) Total number and the ratio of their amount to reaction amount; (2) Length distribution; (3) Reaction participation; (4) Correlated reaction sets (CoSets); (5) interconnectivity degree. Significant discrepancies in above properties were observed during the comparison, which reveals the biological natures of different biological processes. Besides, by demonstrating the application of ExPa analysis on E.coli, we provide a practical guidance of an improved approach to compute ExPas on COBRA models of TRNs.
  
  
  CONCLUSIONS
  ExPas of E.coli's MN, TRN and TTN have different properties, which are strongly connected with various biological natures of biochemical networks, such as topological structure, specificity and redundancy. Our study shows that ExPas are biologically meaningful on the newborn models and suggests the effectiveness of ExPa analysis on them.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Xi
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhao
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000166:cytology / Q000235:genetics* / Q000378:metabolism*; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D014176:Protein Biosynthesis; D015398:Signal Transduction; D014158:Transcription, Genetic