CONICET | Buscador de Institutos y Recursos Humanos

In many biological contexts it is important to understand how the cellsignaling system responds to time-dependent inputs. For example, geneexpression in neuronal cells is affected by the time-dependent signals that thecells receive from their afferent neurons, and this is essential for memoryformation. Signaling pathways stimulated by inputs that change rapidly overtime need to process a significant amount of information. How much informationthey are able to process per unit of time is proportional to itsbandwidth, which is determined by measuring the system?s responseto fluctuating signals at different frequencies. The larger thebandwidth of a pathway, the shorter its response time and the more accuratelyits response to a rapidly varying signal. It has been shown that certain small signalingnetworks behave as low-pass filters where the response ismaximized at the zero input frequency. The focus of the work presented here isto identify conditions for which simple signaling topologies can optimize a givenresponse at certain intermediate input frequencies, where by optimizingwe mean, for example, maximizing the production or the level ofactivation of a protein at these frequencies. We refer to thisoptimization as resonance. Resonance is typically measured in quasi steady-state (long lastingpulsatile signals). By using a combined computational and theoretical approach,we show that resonance can be obtained for pulsatile input signals that areactive for a short number of periods as compared with the time scale of thesignaling component processing that input,. We call this effect transientresonance since the systems we consider donot exhibit resonance when using long lasting pulsatile signal. Transientresonance also emerges for the same signaling system, with the long lastingpulsatile signal, provided that the downstream singling components are fastenough to read pre-steady-state information.