Signal differentiation with genetic networks

Abstract Biological counterparts of differential operators are not only likely to exist in natural systems but also desirable to realize as modules in synthetic biology. Therefore, we present a genetic regulatory network which approximates the differential operator. For the in silico design, we use a modeling framework which takes into account the limitations imposed by finite pools of resources; we synthesize the differentiator module by combining genetic regulatory parts which realize basic input/output functions such as a gain, integrator and signal difference. The resulting two-gene-network approximates the transfer function of a differential operator with additional low-pass filter. The functionality of this small network, as well as its robustness towards changes in the cellular environment, is investigated numerically.