A Cellular Automaton Approach for Efficient Computing on Surface Chemical Reaction Networks

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Sihai Yu, Wenli Xu, Jia Lee, Teijiro Isokawa
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引用次数: 0

Abstract

A surface chemical reaction network (sCRN, Qian and Winfree in DNA Computing and Molecular Programming: 20th International Conference, DNA 20, Kyoto, Japan, September 22–26, 2014. Proceedings 20. Springer, 2014) is an emergent paradigm for molecular programming, in which a chemical molecule is placed at each site of a lattice, and each molecule may undergo either bi-molecular reactions associated with one of the nearest molecules or uni-molecular reactions autonomously. The lattice structure as well as the localized reactions between molecules facilitate an effective formalization of sCRNs in the framework of cellular automata. This formalism not only allows a systematic evaluation of the complexity of a sCRN, but also enables a formal approach to reduce the model’s complexity for the sake of improving its effectiveness. To this end, this paper proposes a new sCRN model that has less complexity measured in terms of the numbers of both cell states and transition rules. Especially, universality of computations will be shown by implementing all asynchronous circuits, including the well-known full-adder, into the sCRN. The decreased complexity may enhance the feasibility of the proposed sCRN model for physical implementation.

Abstract Image

高效计算表面化学反应网络的细胞自动机方法
表面化学反应网络(sCRN,Qian 和 Winfree 在《DNA 计算与分子编程:第 20 届国际会议,DNA 20》(2014 年 9 月 22-26 日,日本京都)上发表。Proceedings 20.Springer,2014)是一种新兴的分子编程范式,其中在晶格的每个位置都放置了一个化学分子,每个分子既可以与最近的一个分子进行双分子反应,也可以自主进行单分子反应。晶格结构以及分子间的局部反应促进了细胞自动机框架中 sCRN 的有效形式化。这种形式化方法不仅可以系统地评估 sCRN 的复杂性,还可以通过形式化方法降低模型的复杂性,从而提高模型的有效性。为此,本文提出了一种新的 sCRN 模型,以单元状态和转换规则的数量来衡量,该模型的复杂度较低。特别是,通过在 sCRN 中实现所有异步电路(包括众所周知的全梯形),将显示计算的普遍性。复杂性的降低可能会增强所提出的 sCRN 模型在物理实现方面的可行性。
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来源期刊
New Generation Computing
New Generation Computing 工程技术-计算机:理论方法
CiteScore
5.90
自引率
15.40%
发文量
47
审稿时长
>12 weeks
期刊介绍: The journal is specially intended to support the development of new computational and cognitive paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems, agent-oriented systems, and cognitive aspects of human embodied knowledge. It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, human cognition and learning, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems. The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process.
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