  Describing a complex system is in many ways a problem akin to identifying an
object, in that it involves defining boundaries, constituent parts and their
relationships by the use of grouping laws. Here we propose a novel method which
extends the use of complex networks theory to a generalized class of
non-Gestaltic systems, taking the form of collections of isolated, possibly
heterogeneous, scalars, e.g. sets of biomedical tests. The ability of the
method to unveil relevant information is illustrated for the case of gene
expression in the response to osmotic stress of {\it Arabidopsis thaliana}. The
most important genes turn out to be the nodes with highest centrality in
appropriately reconstructed networks. The method allows predicting a set of 15
genes whose relationship with such stress was previously unknown in the
literature. The validity of such predictions is demonstrated by means of a
target experiment, in which the predicted genes are one by one artificially
induced, and the growth of the corresponding phenotypes turns out to feature
statistically significant differences when compared to that of the wild-type.
