The focus of this paper is on the simulation of gene expression, based on the observation that stochastic systems tend to respond smoothly to a dynamic stimulus (rapid activation and deactivation), whereas systems with stable gene expression have a much larger variance of response.

The paper itself stresses the simulation of gene expression from the point of view of a biologist; its focus is on the biological relevance of the model, but not on the mathematical model itself. Details about the genetic algorithm used, and its implementation, are neglected, and the formal description of the simulation model is quite sparse.

The paper has limited value for researchers in modeling, discrete mathematics, or bioinformatics, but could be useful for scientists in the life sciences and genetics. It discusses the modeling of gene expression in reaction kinetics, provides empirical values for thresholds, and also includes an ample discussion of the modeling of early and late selection in biological evolution.