权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Modelling approaches to molecular computation

分子计算建模方法

基本信息

批准号：
1781682
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1781682
关键词：
Modelling approaches molecular computation

项目摘要

Molecular computing is a nascent field aiming to study the use of molecules as a substrate for computation. Motivations for this range from developing massively parallel computer systems that begin to approach the Bremermann limit, the limit on computational speed imposed by the uncertainty principle, to the idea of a 'Doctor in a cell', capable of automatic diagnosis and treatment of medical conditions on near-atomic scales. The natural choice is to use biological systems for their self-assembly and far-from-equilibrium properties, but current approaches fall short of the original motivations. This project proposes an alternative approach to address these shortcomings. Many computational systems rely on single tapes or strings to represent programs, with computational evolution implemented by symbol transformations. A seemingly ideal set of systems to emulate include the lambda and pi calculi, their favourable properties including self-containment of individual programs (making parallelisation trivial), and the coding efficiency with which higher level languages such as LISP can be reduced to lambda expressions. It is not possible to engineer these models with current molecular methods. Rather, the aim is to consider and model the properties of more general contextual rule-based replacement systems, but limited to static rulesets. This class of systems includes 1D cellular automata (CA), which will be implemented as a proof of concept with the additional bonus that some elementary CAs are in fact Turing-complete - capable of universalcomputation. Rule application will be implemented via short oligomers, complementary to their targets and conjugated to their replacements, and computation by iterative assembly of generations of ssDNA molecules. Accounting for noise will be a particular challenge.

分子计算是一个新兴的领域，旨在研究使用分子作为计算的基底。其动机包括开发大规模并行计算机系统，开始接近Bremermann极限，即不确定性原理对计算速度的限制，以及“细胞中的医生”的想法，能够在近原子尺度上自动诊断和治疗医疗状况。自然的选择是利用生物系统的自组装和远离平衡的特性，但目前的方法没有达到最初的动机。该项目提出了一种替代方法来解决这些缺点。许多计算系统依赖于单个磁带或字符串来表示程序，计算进化通过符号转换来实现。一组看似理想的系统仿真包括lambda和pi演算，其有利的属性包括独立程序的自包含（使并行化微不足道），以及编码效率，更高级别的语言，如LISP，可以减少到lambda表达式。用目前的分子方法来设计这些模型是不可能的。相反，我们的目的是考虑和建模更一般的上下文规则为基础的替换系统的属性，但仅限于静态规则集。这类系统包括一维元胞自动机（CA），它将被实现为一个概念的证明与额外的奖金，一些基本的CA实际上是图灵完备的通用计算能力。规则应用将通过短寡聚体来实现，与它们的目标互补并与它们的替代物缀合，以及通过ssDNA分子代的迭代组装来计算。噪音的解释将是一个特别的挑战。