Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

TLDR

We built a graph model of 545 components and 1,259 interactions in hippocampal CA1 neurons and used graph‑theory methods to analyze ligand‑induced signal flow, identifying functional modules through specified input and output nodes. The network analysis revealed regulatory motifs—positive/negative feedback and feedforward loops—centered on highly connected plasticity regulators, suggesting these motifs influence the balance between homeostasis and plasticity.

Abstract

We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.