  We present a novel method for identifying a biochemical reaction network
based on multiple sets of estimated reaction rates in the corresponding
reaction rate equations arriving from various (possibly different) experiments.
The current method, unlike some of the graphical approaches proposed in the
literature, uses the values of the experimental measurements only relative to
the geometry of the biochemical reactions under the assumption that the
underlying reaction network is the same for all the experiments.
  The proposed approach utilizes algebraic statistical methods in order to
parametrize the set of possible reactions so as to identify the most likely
network structure, and is easily scalable to very complicated biochemical
systems involving a large number of species and reactions. The method is
illustrated with a numerical example of a hypothetical network arising form a
"mass transfer"-type model.
