Models of optically-based biological aerosol sensors may help to predict baseline performance and support efficient sensor optimization. Reducing a sensor’s false positive rate while maintaining sensitivity is an important performance goal that must be optimized. To that end, the capacity to theoretically test environmental backgrounds, in an accelerated fashion, would be valuable. Sensor false positives are presumed to occur as a result of complicated transient fluctuations in the environmental aerosol background. Simulating a sensor’s response to such naturally occurring transients, with an appropriate model, is a mechanism for accelerating sensor characterization. These models complement and reduce the need for experimentally challenging interferant tests. Additionally, validated models include the ability to characterize sensor responses to harmful agents or rare materials while simultaneously adjusting many transient parameters. We describe a model of the Lincoln Laboratory Biological Agent Warning Sensor (BAWS), highlighting our general approach to sensor model architecture. The resulting model was utilized to simulate the sensor’s response to a variety of individual background constituents as well as to time varying backgrounds with multiple constituents. The result of the simulation predicts the sensor’s false positive rate to a simulated indoor and outdoor aerosol background, which can be compared to experimental data. Model applications and improvements will be discussed.