A simulation study on gene expression regulation via stochastic model

Gene expression governs the life processes. Understanding gene regulation systems is a key goal of systems biology. There have been some well-established models attempt to build up transcriptional regulatory circuits by analyzing microarray or RNA-Seq data. Most current high-throughput gene expression data were measured with a relatively large amount of cells. Data obtained by single-molecule techniques showed that gene in each cell expressed in a stochastic manner. We establish a gene expression regulation model based on two-state model and conduct a series of simulation experiments to study the performance of this model. We also investigate the effects of model parameters on the simulated data via simple regulatory relations. These simulation results, although still at their preliminary stage, provide a basis for building comprehensive model for gene expression regulation system with multiple genes. Such a forward-engineering model will be crucial for the decryption of real gene regulation systems from both high-throughput transcriptome data and single-cell gene expression data.