Molecular Neural Networks for Detection and Control

用于检测和控制的分子神经网络

• 批准号: 2744387
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2744387
• 关键词: Molecular; Neural; Networks; Detection; Control

The main research question is to discover the necessary components for computation in molecular neural networks and present possible approaches to implement these systems in the context of synthetic biology and DNA nanotechnology. The research will also address the question of learning and autonomy within these systems and determine its importance within implementation of the networks. I will discuss what the basic requirements are for these networks and propose a path towards implementing these networks experimentally through DNA-based computing. Each objective will contribute to the overall aim of this project.The aim of my PhD is to realise proof of principle implementations of scalable chemical molecular neural networks and develop a computational pipeline for the complete development of such networks from abstract specification.Within this, I aim to develop chemical molecular recurrent neural networks with the ability to perform different objectives, such as classification of static input signatures, temporary pattern recognition in a time series or the ability to generate specific pattern outputs from inputs.By training neural networks in-silico and transferring their learned weights in-vitro, molecular networks should be able to perform the same computations. Applications are, for example, in in-vitro cancer diagnostics, where molecular neural networks could be created that could directly classify samples into healthy or cancerous tissues, depending on the presence or absence of specific biomarkers.Objective 1: Design, modelling and simulation of molecular perceptrons and molecular neural networks.Within this objective the basic building blocks of molecular neural networks, perceptrons will be created and analysed. By evaluating different multiplier, threshold, and reporting designs, we can discover which design choices produce the most effective molecular neural network results.Objective 2: Implementation and training of complete feed-forward and recurrent molecular neural networks.With the basic design decisions of molecular perceptrons and neural networks decided, we will design a small library of molecular neural networks: feed-forward networks for classification of static input patterns and recurrent networks for classification and/or generation of time series data. The networks' performance of learning different molecular signatures will be assessed through simulation.Objective 3: Computational pipeline to construct molecular networks as a strategy to shorten nanotechnology development cycles.We will develop a computational pipeline that automatically assembles a functional molecular neural network from the specification provided by the user. Given an abstract network specification, the pipeline will optimize network weights and chemical reaction rates to perform the desired computation, compile a DNA strand displacement system that implements the network, and finally produce DNA sequences for each molecular perceptron.I aim to produce at least 2 research papers, and a review article with the work produced in this project. In addition, I hope to create open-source software that is useful for other researchers who want to create molecular neural networks.

主要的研究问题是发现分子神经网络中计算的必要组件，并提出在合成生物学和DNA纳米技术的背景下实现这些系统的可能方法。研究还将解决这些系统中的学习和自主性问题，并确定其在网络实施中的重要性。我将讨论这些网络的基本要求是什么，并提出一个通过基于DNA的计算实验实现这些网络的路径。每个目标都将有助于这个项目的总体目标。我的博士学位的目的是实现可扩展的化学分子神经网络的原理实现的证明，并开发一个计算管道，用于从抽象规范完成这种网络的开发。在此范围内，我的目标是开发具有执行不同目标的能力的化学分子递归神经网络，例如静态输入签名的分类，时间序列中的临时模式识别或从输入中生成特定模式输出的能力。通过在计算机上训练神经网络并在体外转移其学习的权重，分子网络应该能够执行相同的计算。应用例如在体外癌症诊断中，其中可以创建分子神经网络，该分子神经网络可以根据特定生物标志物的存在或不存在将样品直接分类为健康组织或癌组织。分子感知器和分子神经网络的设计、建模和仿真。在这个目标下，将创建和分析分子神经网络的基本构件感知器。通过评估不同的乘法器、阈值和报告设计，我们可以发现哪些设计选择产生最有效的分子神经网络结果。目标2：完全前馈和递归分子神经网络的实现和训练在确定了分子感知器和神经网络的基本设计决策之后，我们将设计一个小型的分子神经网络库：用于静态输入模式分类的前馈网络和用于时间序列数据分类和/或生成的递归网络。目标3：构建分子网络的计算管道，作为缩短纳米技术开发周期的策略我们将开发一个计算管道，根据用户提供的规格自动组装功能分子神经网络。给定一个抽象的网络规范，流水线将优化网络权重和化学反应速率以执行所需的计算，编译实现网络的DNA链置换系统，并最终为每个分子感知器生成DNA序列。我的目标是至少发表2篇研究论文，以及一篇评论文章。此外，我希望创建开源软件，对其他想要创建分子神经网络的研究人员有用。