{"title":"Computing with Cells: Membrane Systems","authors":"O. Ibarra","doi":"10.1109/I-SPAN.2008.12","DOIUrl":null,"url":null,"abstract":"Summary form only given. Membrane computing, first introduced in 1998 by Gheorghe Paun, is a part of the general research effort of describing and investigating computing models, ideas, architectures, and paradigms from the processes taking place in nature. It is a branch of molecular computing that is motivated by cell biology. Membrane computing identifies an unconventional computing model, namely a P system, which abstracts from the way living cells process chemical compounds in their compartmental structure. Regions defined by a membrane structure contain multisets of objects that evolve according to specified rules. The objects can be represented as symbols or strings of symbols. By using the rules in a nondeterministic (deterministic) maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one region to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. P systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future bio-technology gives way to a practical bio- realization. Since its introduction, the literature in this area has grown rapidly (in 2003, the Institute for Scientific Information designated the initialpaper as \"fast breaking\" and the domain as an \"emerging research front in computer science\"). We give a brief overview of membrane computing and report on recent results that answer some interesting and fundamental open questions in the field. We also look at the recently introduced neural-like systems, called spiking neural P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We present various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind counter machines. The latter devices are known to be equivalent to vector addition systems (or Petri nets) and, hence, have many decidable properties.","PeriodicalId":305776,"journal":{"name":"2008 International Symposium on Parallel Architectures, Algorithms, and Networks (i-span 2008)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on Parallel Architectures, Algorithms, and Networks (i-span 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SPAN.2008.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Summary form only given. Membrane computing, first introduced in 1998 by Gheorghe Paun, is a part of the general research effort of describing and investigating computing models, ideas, architectures, and paradigms from the processes taking place in nature. It is a branch of molecular computing that is motivated by cell biology. Membrane computing identifies an unconventional computing model, namely a P system, which abstracts from the way living cells process chemical compounds in their compartmental structure. Regions defined by a membrane structure contain multisets of objects that evolve according to specified rules. The objects can be represented as symbols or strings of symbols. By using the rules in a nondeterministic (deterministic) maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one region to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. P systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future bio-technology gives way to a practical bio- realization. Since its introduction, the literature in this area has grown rapidly (in 2003, the Institute for Scientific Information designated the initialpaper as "fast breaking" and the domain as an "emerging research front in computer science"). We give a brief overview of membrane computing and report on recent results that answer some interesting and fundamental open questions in the field. We also look at the recently introduced neural-like systems, called spiking neural P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We present various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind counter machines. The latter devices are known to be equivalent to vector addition systems (or Petri nets) and, hence, have many decidable properties.
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细胞计算:膜系统
只提供摘要形式。膜计算于1998年由georghe Paun首次提出,是描述和调查计算模型、思想、架构和范式的一般研究工作的一部分,这些模型、思想、架构和范式来自自然界中发生的过程。它是分子计算的一个分支,受到细胞生物学的启发。膜计算确定了一种非常规的计算模型,即P系统,它从活细胞在其隔室结构中处理化合物的方式中抽象出来。由膜结构定义的区域包含多组根据特定规则演化的对象。对象可以表示为符号或符号串。通过以非确定性(deterministic)最大并行方式使用规则,可以获得系统配置之间的转换。转换序列是对系统如何演化的计算。研究人员研究了控制物体从一个区域转移到另一个区域并应用这些规则的各种方法,以及溶解、分裂或产生膜的可能性。一旦未来的生物技术让位于实际的生物实现,P系统在以一种有效的方式实现大规模并发系统方面具有巨大的潜力,这将使我们能够解决当前棘手的问题。自引入以来,该领域的文献迅速增长(2003年,科学信息研究所将最初的论文指定为“快速突破”,并将该领域指定为“计算机科学的新兴研究前沿”)。我们给出了膜计算的简要概述,并报告了最近的结果,回答了一些有趣的和基本的开放性问题。我们还研究了最近引入的类神经系统,称为脉冲神经P系统。这些系统将脉冲神经元的思想融入到膜计算中。我们介绍了不同的类,并描述了它们的计算能力和复杂性。特别地,我们分析了异步和顺序系统,并提出了它们成为(非)通用的一些条件。非泛型的特征是单调计数机和部分盲计数机。众所周知,后一种装置相当于矢量加法系统(或Petri网),因此具有许多可确定的性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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