Modeling Signal Transduction in Classical Conditioning with Network Motifs - Keifer, Joyce; Houk, James C. (Frontiers in Molecular Neuroscience, 2011)
Biological networks are constructed of repeated simplified patterns, or modules, called network motifs. Network motifs can be found in a variety of organisms including bacteria, plants and animals, as well as intracellular transcription networks for gene expression and signal transduction processes in neuronal circuits. Standard models of signal transduction events for synaptic plasticity and learning often fail to capture the complexity and cooperativity of the molecular interactions underlying these processes. Here, we apply network motifs to a model for signal transduction during an in vitro form of eyeblink classical conditioning that reveals an underlying organization of these molecular pathways. Experimental evidence suggests there are two stages of synaptic AMPA receptor (AMPAR) trafficking during conditioning. Synaptic incorporation of GluR1-containing AMPARs occurs early to activate silent synapses conveying the auditory conditioned stimulus (CS) and this initial step is followed by delivery of GluR4 subunits that supports acquisition of learned conditioned responses (CRs). Overall, the network design of the two stages of synaptic AMPAR delivery during conditioning describes a coherent feedforward loop (C1-FFL) with AND logic. The combined inputs of GluR1 synaptic delivery AND the sustained activation of 3-phosphoinositide-dependent protein-kinase-1 (PDK-1) results in synaptic incorporation of GluR4-containing AMPARs and the gradual acquisition of CRs. The network architecture described here for conditioning is postulated to act generally as a sign-sensitive delay element that is consistent with the nonlinearity of the conditioning process. Interestingly, this FFL structure also performs coincidence detection. A motif-based approach to modeling signal transduction can be used as a new tool for understanding molecular mechanisms underlying synaptic plasticity and learning and for comparing findings across forms of learning and model systems.