The mission of CeTICS is to discover and chemically characterize molecular targets of venom toxins, which very likely initiate biological responses of interest in human pathophysiology and therapeutics. In pursuing this mission, CeTICS sub-projects generate a vast amount of heterogeneous, low and high- throughput omics data, whose complexity defy analytical efforts to uncover hidden biological knowledge. Along an analytical line, there is initially a need for storage and organization of these produced big data in an in-house repository with suitable categorization, security and access control. Ultimately, these omics data can yield molecular static maps, which are diagrams describing interactions between chemical species in a given biological context. However, static maps are not sufficient to unveil underlying dynamics of cell signaling pathways, since they only allow a qualitative combinatorial assessment of chemical species. Thus, it is necessary to construct quantitative dynamic models of molecular signaling networks present in these maps. Therefore, we proposed the ARTISiN platform, an amalgam of repositories and tools, both public and in house built ones, for identification of signaling networks and assessment of mechanistic aspects of their actions. ARTISiN allows storage and organization of low and high-throughput omics data, production of static maps of signaling networks, as well as assistance for designing, adjustment and simulation of dynamic models of sub- networks extracted from these maps.