  Fluctuations in the abundance of molecules in the living cell may affect its
growth and well being. For regulatory molecules (e.g., signaling proteins or
transcription factors), fluctuations in their expression can affect the levels
of downstream targets in a network. Here, we develop an analytic framework to
investigate the phenomenon of noise correlation in molecular networks.
Specifically, we focus on the metabolic network, which is highly inter-linked,
and noise properties may constrain its structure and function. Motivated by the
analogy between the dynamics of a linear metabolic pathway and that of the
exactly soluable linear queueing network or, alternatively, a mass transfer
system, we derive a plethora of results concerning fluctuations in the
abundance of intermediate metabolites in various common motifs of the metabolic
network. For all but one case examined, we find the steady-state fluctuation in
different nodes of the pathways to be effectively uncorrelated. Consequently,
fluctuations in enzyme levels only affect local properties and do not propagate
elsewhere into metabolic networks, and intermediate metabolites can be freely
shared by different reactions. Our approach may be applicable to study
metabolic networks with more complex topologies, or protein signaling networks
which are governed by similar biochemical reactions. Possible implications for
bioinformatic analysis of metabolimic data are discussed.
