Development of a neuron model based on DNAzyme regulation

Synthetic biological neural networks: From current implementations to future perspectives

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

Developing a three-input cascade DNA logic gate based on the biological characteristics of metal ion-GO, combined with analysis and verification

Due to the limitation of technology, electronic computing is approaching the limit of technology, and new computing tools need to be developed. Here, we build a three-input cascade logic gate based on the advantages of biomolecules, particularly DNA, in the construction of computational logic systems, combined with metal ions and graphene oxide (GO). It is worth mentioning that this study uses a variety of research methods. In addition to the commonly used biological experiments, NUPACK and visual DSD simulation methods are used for analysis, and orthogonal, standardized and other statistical means are used to simplify the experimental process and make the results intuitive. Finally, the designed three-input logic gate is successfully constructed, and it is found that it may have the potential to realize complex computing.