Tunable filtering of chemical signals in a simple nanoscale reaction-diffusion network

We study numerically the filtering capabilities of a nanoscale network of two micrometer-sized containers joined by a nanotube, one of which hosts an enzymatic chemical reaction. Spatiotemporal chemical signals of substrate molecules are injected into the network. The substrate propagates by diffusion and reacts with enzymes distributed in the network prior to the injections. The dimensions of the network are tailored in a way that the transport and enzymatic reaction rates are comparable in size, a situation in which the overall behavior is highly influenced by the geometry and topology of the network. This property is crucial for the functionality of the filter developed in here. It is demonstrated that input signals can be classified in a crude way using a simple setup (a two-container network) and that the classification can be tuned by changing the geometry of the network (the length of the tube connecting the two containers). The filter device we investigate can also be viewed as a primitive chemistry-based computational element in the sense that the information encoded in the signals is processed using chemical reactions. In particular, it is demonstrated that the two-container device may filter out signals based on the average injection frequency.