An Adaptive Incremental Stochastic Simulation Algorithm (iSSA) for Behavioral Design Verification in Synthetic Biology

C. Madsen
Chris J. Winstead, Utah State University
C. Myers
A. Tejeda
E. Monzon

Originally published by the American Institute of Physics AIP in Journal of Chemical Physics.


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.