Promoters with multiple binding sites present a regulatory mechanism of several natural biological systems. It has been shown that such systems reflect a higher stability in comparison to the systems with small numbers of binding sites. Regulatory mechanisms with multiple binding sites are therefore used more frequently in artificially designed biological systems in recent years. While the number of possible promoter states increases exponentially with the number of binding sites, it is extremely hard to model such systems accurately. Here we present an adaptation of stochastic simulation algorithm for accurate modeling of gene regulatory networks with multiple binding sites. Small computational complexity of adapted algorithm allows us to model any feasible number of binding sites per promoter. The approach introduced in this work is demonstrated on the model of switching mechanism in Epstein-Barr virus, where 20 binding sites are observed on one of the promoters. We show that the presented approach is easy to adapt to any biological systems based on the regulatory mechanisms with multiple binding sites in order to obtain and analyze their behavior.
Keywords: computational modeling; gene regulatory networks; multiple binding sites; multiscale stochastic simulation algorithm; systems biology.