  This paper proposes the use of Eukaryotic cells, namely astrocytes, to
develop logic gates. The logic gates are achieved by manipulating the threshold
of Ca$^{2+}$ ion flows between the cells, based on the input signals. Through
wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17
genes, we show that both AND and OR gates can be implemented by controlling
Ca$^{2+}$ signals that flow through the population. A reinforced learning
platform is also presented in the paper to optimize two main parameters, which
are the Ca$^{2+}$ activation threshold and time slot of input signals $T_b$
into the gate. This design platform caters for any size and connectivity of the
cell population, by taking into consideration the delay and noise produced from
the signalling between the cells, in order to fine-tune the activation
threshold and input signal time slot parameters. To validate the effectiveness
of the reinforced learning platform, a Ca$^{2+}$ Signalling-based Molecular
Communications Simulator was used to simulate the signalling between the
astrocyte cells. The results from the simulation showed that an optimum value
for both the Ca$^{2+}$ activation threshold and time slot of input signals
$T_b$ is required to achieve optimal computation accuracy, where up to 90\%
accuracy for both the AND and OR gates can be achieved with the right
combination of values. The reinforced learning platform for the engineered
astrocytes to create digital logic gates can be used for future
Neural-Molecular Computing chip, which can revolutionize brain implants that
are constructed from engineered biological cells.
