  Fluctuations in the measured mRNA levels of unperturbed cells under fixed
conditions have often been viewed as an impediment to the extraction of
information from expression profiles. Here, we argue that such expression
fluctuations should themselves be studied as a source of valuable information
about the underlying dynamics of genetic networks. By analyzing microarray data
taken from Saccharomyces cerevisiae, we demonstrate that correlations in
expression fluctuations have a highly statistically significant dependence on
gene function, and furthermore exhibit a remarkable scale-free network
structure. We therefore present what we view to be the simplest
phenomenological model of a genetic network which can account for the presence
of biological information in transcript level fluctuations. We proceed to
exactly solve this model using a path integral technique and derive several
quantitative predictions. Finally, we propose several experiments by which
these predictions might be rigorously tested.
