2024/03/13 by Samitha Somathilaka, Adrian Ratwatte, Somathilaka, Samitha +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Cell Image Analysis Techniques #Computational Drug Discovery Methods #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning in Materials Science #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2403.08549
openalex publication_date 2024/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.