  In our earlier work, we introduced the concept of Gene Regulatory Neural
Network (GRNN), which utilizes natural neural network-like structures inherent
in biological cells to perform computing tasks using chemical inputs. We define
this form of chemical-based neural network as Wet TinyML. The GRNN structures
are based on the gene regulatory network and have weights associated with each
link based on the estimated interactions between the genes. The GRNNs can be
used for conventional computing by employing an application-based search
process similar to the Network Architecture Search. This study advances this
concept by incorporating cell plasticity, to further exploit natural cell's
adaptability, in order to diversify the GRNN search that can match larger
spectrum as well as dynamic computing tasks. As an example application, we show
that through the directed cell plasticity, we can extract the mathematical
regression evolution enabling it to match to dynamic system applications. We
also conduct energy analysis by comparing the chemical energy of the GRNN to
its silicon counterpart, where this analysis includes both artificial neural
network algorithms executed on von Neumann architecture as well as neuromorphic
processors. The concept of Wet TinyML can pave the way for the new emergence of
chemical-based, energy-efficient and miniature Biological AI.
