Organizes and stores in a structured format signaling information published in the scientific literature. The captured information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. The entire network can be freely downloaded and used to support logic modeling or to interpret high content datasets. The core of this project is a collection of more than 11000 manually-annotated causal relationships between proteins that participate in signal transduction. Each relationship is linked to the literature reporting the experimental evidence. In addition each node is annotated with the chemical inhibitors that modulate its activity. The signaling information is mapped to the human proteome even if the experimental evidence is based on experiments on mammalian model organisms.