Molecular Neural Networks for Detection and Control

主要的研究问题是发现分子神经网络中计算所需的组件，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。这项研究还将讨论这些系统内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，并提出一条通过基于DNA的计算以试验性方式实现这些网络的途径。每个目标都将有助于本项目的总体目标。我的博士学位的目标是实现可扩展化学分子神经网络的原理实现的证明，并从抽象规范开发一条完整的计算管道。在此期间，我的目标是开发能够执行不同目标的化学分子递归神经网络，例如对静态输入信号的分类、时间序列中的临时模式识别或从输入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传递其学习的权重，分子网络应该能够执行相同的计算。目标1：分子感知器和分子神经网络的设计、建模和模拟。在这个目标中，感知器和分子神经网络是分子神经网络的基本组成部分。通过评估不同的乘子、阈值和报告设计，我们可以发现哪些设计方案能够产生最有效的分子神经网络结果。目标2：完整的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后，我们将设计一个小型分子神经网络库：用于静态输入模式分类的前馈网络和用于分类和/或生成时间序列数据的递归网络。目标3：构建分子网络的计算流水线作为缩短纳米技术开发周期的策略，我们将开发一种计算流水线，根据用户提供的规范自动组装功能分子神经网络。给出一个抽象的网络规范，管道将优化网络权重和化学反应速率以执行所需的计算，编译实现该网络的DNA链置换系统，并最终产生每个分子感知器的DNA序列。我的目标是产生至少2篇研究论文和一篇评论文章。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。

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.