An Adaptive Incremental Stochastic Simulation Algorithm (iSSA) for Behavioral Design Verification in Synthetic Biology
This paper presents a new, adaptive incremental stochastic simulation algorithm (iSSA) for visualizing stochastic simulation results in synthetic biological systems, particularly genetic circuits. Stochastic models are increasingly important in the do- main of genetic circuits, where micro-scale random fluctuations in gene activity may induce significant changes in the circuit’s macro-scale behavior. As part of the de- sign verification process, designers often compute statistical measures for a chemical signal, such as the mean and standard deviation of a molecular signal at a particu- lar time. In some important cases, these statistical measures may predict fictitious behaviors. The iSSA is a method for selecting representative results and excluding outliers from among a collection of stochastic simulations. By selecting results from traditional SSA simulations, the iSSA method avoids possible distortions that may arise from statistical processing. The method returns a single (or set of) SSA simula- tion path that is representative of typical system behavior. Deriving a representative set of trace results is useful as a first stage of rapid verification for a complex system, thereby improving productivity in early-stage design exploration. The iSSA delivers a simplified visualization of stochastic results which relaxes the burden of expertise for the end user.