Abstract. Gene networks that control gene expression play a primary role in determining the epigenetic and biochemical properties of cells. At the same time, the interconnectedness of gene networks with networks of other types and levels requires approaches that consider this complexity. The article addresses this problem by examining the nesting of gene networks within cells, which themselves are nodes of a higher-level network, namely, a cell ensemble. Information models (object, relational, and object-relational) of the control gene network over genetic blocks are created, as well as a model of the cell ensemble as a network over cell control gene networks. The combined two-level model describes the gene network structures within cell ensembles with the dynamic characteristics of the main molecular genetic processes. Based on these models, the SETIES platform was developed for in silico studies of molecular genetic systems controlling gene expression in prokaryotes and eukaryotes. The platform includes software components for the design and visualization of cell and intercell gene networks, the automatic construction of their mathematical models, and the simulation of network dynamics in cell-free systems, cell lines, and clonal cell populations, as well as in cell ensembles with constant and dynamic structure. The computational modules implement the formalism of generalized threshold models, combining the advantages of discrete and continuous methods for modeling these systems. Among the tasks addressed are the construction of gene activity diagrams over time, the calculation of kinetic curves for mRNA and protein concentrations, the identification of gene expression patterns as functional states of the gene network, the assessment of parametric stability of regimes, and the simulation of mechanisms of epigenetic inheritance, phenotypic heterogeneity, and morphogenetic positioning. Overall, the platform implements the "network of networks" paradigm, which serves as the basis for advanced research in systems and synthetic biology.