  Often in bacterial regulatory networks, small noncoding RNAs (sRNA) interact
with several mRNA species. The competition among mRNAs for binding to the same
pool of sRNA might lead to crosstalk between the mRNAs. This is similar to the
competing endogenous RNA (ceRNA) effect wherein the competition to bind to the
same pool of miRNA in Eukaryotes leads to miRNA mediated crosstalk resulting in
subtle and complex gene regulation with stabilised gene expression. We study an
sRNA-driven feedforward loop (sFFL) where the top-tier regulator, an sRNA
(RprA), translationally activates the target protein (RicI) directly and also,
indirectly, via up-regulation of its transcriptional activator (RpoS/sigma^s).
We show that the sRNA-mediated crosstalk between the two mRNA species leads to
maximum target protein synthesis for low synthesis rates of RpoS-mRNA. This
indicates the possibility of an optimal target protein synthesis with efficient
utilisation of RpoS-mRNA which is typically associated with various other
stress response activities inside the cell. Since gene expression is inherently
stochastic due to the probabilistic nature of various molecular interactions
associated with it, we next quantify the fluctuations in the target protein
level using generating function-based approach and stochastic simulations. The
coefficient of variation that provides a measure of fluctuations in the
concentration shows a minimum under conditions that also correspond to optimal
target protein synthesis. This prompts us to conclude that, in sFFL, the
crosstalk leads to optimal target protein synthesis with minimal noise and
efficient utilisation of RpoS-mRNA.

Molecular Networks;Statistical Mechanics