In this paper, a logic computing model was constructed using a DNA nanoparticle, combined with color change technology of DNA/Au nanoparticle conjugates, and DNA computing. Several important technologies are utilized in this molecular computing model: DNA self-assembly, DNA/Au nanoparticle conjugation, and the color change resulting from Au nanoparticle aggregation. The simple logic computing model was realized by a color change, resulting from changing of DNA self-assembly. Based on this computing model, a set of operations computing model was also established, by which a simple logic problem was solved. To enlarge the applications of this logic nanocomputing system, a molecular detection method was developed for H1N1 virus gene detection.
In this study,the DNA logic computing model is established based on the methods of DNA self-assembly and strand branch migration.By adding the signal strands,the preprogrammed signals are released with the disintegrating of initial assembly structures.Then,the computing results are able to be detected by gel electrophoresis.The whole process is controlled automatically and parallely,even triggered by the mixture of input signals.In addition,the conception of single polar and bipolar is introduced into system designing,which leads to synchronization and modularization.Recognizing the specific signal DNA strands,the computing model gives all correct results by gel experiment.
Because of the simplicity of cells, the key to building biological computing systems may lie in constructing distributed systems based on cell–cell communication. Guided by a mathematical model, in this study we designed,simulated, and constructed a genetic double-branch structure in the bacterium Escherichia coli. This genetic double-branch structure is composed of a control cell and two reporter cells.The control cell can activate different reporter cells according to the input. Two quorum-sensing signal molecules, 3OC12-HSL and C4-HSL, form the wires between the control cell and the reporter cells. This study is a step toward scalable biological computation, and it may have many potential applications in biocomputing, biosensing, and biotherapy.
