title: A possibilistic framework for constraint-based metabolic flux analysis.
abstract: BACKGROUND
  Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.
  
  
  RESULTS
  Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way.
  
  
  CONCLUSIONS
  We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.
authors: Instituto de Automática AI2, Universidad Politécnica de Valencia, Camino de Vera s/n 46022, Spain. frallaes@doctor.upv.es
authors: Francisco
authors: F
authors: Llaneras
authors: Antonio
authors: A
authors: Sala
authors: Jesús
authors: J
authors: Picó
mesh_terms: D048230:Corynebacterium glutamicum / Q000378:metabolism; D055432:Metabolomics / Q000379:methods*; D009010:Monte Carlo Method