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Background  Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix ( S ). Singular value decomposition (SVD) of the ExPa matrix  P  has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices.    Results  We show that the SVD of a matrix ( W ) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of  P . This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks.    Conclusion  These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling.   Contact :  palsson@ucsd.edu

Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition