Dataset Information


Deterministic characterization of stochastic genetic circuits.

ABSTRACT: 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.


PROVIDER: S-EPMC1863463 | BioStudies | 2007-01-01T00:00:00Z

REPOSITORIES: biostudies

Similar Datasets

1000-01-01 | S-EPMC3181209 | BioStudies
1000-01-01 | S-EPMC2996131 | BioStudies
2014-01-01 | S-EPMC3907303 | BioStudies
1000-01-01 | S-EPMC3733900 | BioStudies
1000-01-01 | S-EPMC5154471 | BioStudies
2010-01-01 | S-EPMC2955726 | BioStudies
2006-01-01 | S-EPMC1464336 | BioStudies
1000-01-01 | S-EPMC2141890 | BioStudies
2007-01-01 | S-EPMC1894820 | BioStudies
2013-01-01 | S-EPMC3827321 | BioStudies