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Biochemical signal transduction, a form of molecular communication, can bemodeled using graphical Markov channels with input-modulated transition rates.Such channel models are strongly non-Gaussian. In this paper we use a linearnoise approximation to construct a novel class of Gaussian additive white noisechannels that capture essential features of fully- and partially-observedintensity-driven signal transduction. When channel state transitions that aresensitive to the input signal are directly observable, high-frequencyinformation is transduced more efficiently than low-frequency information, andthe mutual information rate per bandwidth (spectral efficiency) issignificantly greater than when sensitive transitions and observabletransitions are disjoint. When both observable and hidden transitions areinput-sensitive, we observe a superadditive increase in spectral efficiency.

Linear Noise Approximation of Intensity-Driven Signal Transduction Channels