Molecular logic computing model based on DNA self-assembly strand branch migration

Abstract

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. In recent years, with the approaching of the Moore's Law, the pressure of traditional electronic computers increased greatly for handling mass information. The interests of researchers have been attracted to the area of novel computing. Taking advantage of some new methods of quantum and molecular computing, researchers attempt to use nano-materials and technologies to implement super large scale information processing. In particular, DNA computing has become a research focus in molecular computing, combined with information science, biology and nanotechnology [1][2][3][4][5]. Because the DNA molecules have lots of natural advantages in huge parallelism and microscopic, the mass parallel information processing could be achieved by using DNA computing. Thus, DNA computing may become a flourishing route in future computing. In fact, since the end of last century, it has made a great progress, both in theoretical models and experimental operations of DNA computing. In addition, it has developed greatly in the interdisciplinary fields of information processing, molecular encoding, nanomachines and so on [6][7][8][9][10][11]. In the development of DNA computing, a variety of molecular operations have been utilized such as polymerase chain reaction (PCR), fluorescence techniques, strand branch migration and self assembly techniques. In these methods, DNA strand branch migration with fluorescent labeling develops rapidly for constructing various molecular computing models [12][13][14][15]. Importantly, Professor Winfree used DNA strand branch migration to implement simple squareroot computing and neural networks computing in 2011. These works were reported in Science and Nature, and attracted lots of attentions from researchers in the field of information computing [4,5]. DNA strand branch migration is able to be combined with not only fluorescent detecting and DNA self-assembly, but also nano-particles, quantum dots and proteins. Moreover, it has promoted the development of research fields as parallel computing, cryptography and nanoelectronics.

An Image Encryption Algorithm Based on Chaos System and DNA Strand Displacement Model

“…In 2011, professor Winfree using four neurons achieved a game of "read your mind" based on DNA strand displacement [7]. In 2013, Zhang proposed a calculation model of logical gate and logical OR gate and introduced the concept single polar and bipolar [8].…”

8-Bit Adder and Subtractor with Domain Label Based on DNA Strand Displacement

“…However, dual-rail logic is not suitable for large-scale cascaded molecular logic circuits. In 2013, Zhang proposed and verified the logical “AND” gate and “OR” gate [ 20 ]. In 2014, Guo designed multiple types of logic gate based on a single g-quadrupled DNA strand [ 21 ].…”

Model Checking Temporal Logic Formulas Using Sticker Automata

“…Many other DNA models have been constructed, such as the restricted model [5], the sticker system [6], the length-encoding model [7], the sticker automaton model [8], the DNA Turing machine model [9], the nonenumerative DNA model [10], the giant-magneto-resistance-based DNA model [11], the logical DNA molecular model [12], and the logical nanomolecular model [13]. And a series of methods based on nonautonomous or self-assembling are proposed for solving various complex computational problems, including the Nondeterministic Polynomial (NP) ones.…”