  For cellular biochemical reaction systems where the numbers of molecules is
small, significant noise is associated with chemical reaction events. This
molecular noise can give rise to behavior that is very different from the
predictions of deterministic rate equation models. Unfortunately, there are few
analytic methods for examining the qualitative behavior of stochastic systems.
Here we describe such a method that extends deterministic analysis to include
leading-order corrections due to the molecular noise. The method allows the
steady-state behavior of the stochastic model to be easily computed,
facilitates the mapping of stability phase diagrams that include stochastic
effects and reveals how model parameters affect noise susceptibility, in a
manner not accessible to numerical simulation. By way of illustration we
consider two genetic circuits: a bistable positive-feedback loop and a
negative-feedback oscillator. We find in the positive feedback circuit that
translational activation leads to a far more stable system than transcriptional
control. Conversely, in a negative-feedback loop triggered by a
positive-feedback switch, the stochasticity of transcriptional control is
harnessed to generate reproducible oscillations.
