Artificial Intelligence Tools For Automatic Single Molecule Analysis

用于自动单分子分析的人工智能工具

• 批准号: BB/R022143/1
• 负责人: Richard Barrett-Jolley
• 金额: $ 19.18万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR022143%2F1
• 关键词: Artificial; Intelligence; Tools; Automatic; Single

*What will we do?*Develop and distribute an "artificial intelligence" application to allow fellow scientists to analyze a type of data ("single molecule data") difficult to analyse with existing methods. *Machines can learn, but they take a lot of training*Machines can recognise speech in devices from Amazon's "Alexa" to call centres using artificial intelligence ("AI"). As an example, just ask Alexa what "AI" is, and she will tell you. This has only been possible recently as computers have become sufficiently powerful. The technologies to do it are collectively called "machine learning". One advantage of such "machines" is that they can answer questions unsupervised by people. One limitation is that they require enormous labelled datasets to "learn" in the first place. This is called "training data". We have devised, a "trick" to generate massive datasets, by playing simulated data into recording apparatus and then recording back the resulting signal. Because we control the entire process the data is inherently "labelled" in the way necessary to train intelligent machines. With this technique together with the use of Google Brain's freely available "TensorFlow" AI library, we can create applications that analyze data for us.*Why "Single Molecules"*Many molecules found in animal cells behave as switches. Their individual "on" or "off" state can then be measured as either pulses of light or electrical current giving real-time mechanistic insight. They are important throughout biology with the estimated Global market for drugs targeting one family of these molecular switches (ion channels) alone being $11.5bn. The flip-side is that experiments measuring these "switches" generate big datasets that are difficult and laborious to analyse.*Could single molecule biology contribute to tackling diseases of age, climate change, anti-bacterial resistance and global terrorism?*In short "Yes": Ion channel malfunction in particular, is responsible for many age-related diseases. Interest from Pharma is enormous because they are targets for many drugs from sedatives to heart medicines. Certain insecticides act via their ion channels, but these are toxic to people too. One such agent, the nerve toxin "VX" hit the news when it was used in the assassination of Kim Jong-Nam. Even the relatively safe insect repellent citronella repels mosquitoes by activating ion channels. Permethrin-resistant mosquitoes are resistant because they have a specific mutation in an ion channel creating a real problem in malaria control. Plants too express a range of ion channels with critical roles including salt regulation. Since climate change is increasing the salination of many agricultural regions, there is keen interest in whether biological modification of root ion channels could promote survival of crops in salt-rich soils. Ion channels have also been studied in synthetic biology because they can be activated by chemicals at concentrations far lower than that of other sensors. Many uses have been proposed for these, such as detection of explosives, biological weapons, narcotics or certain diseases. A recent discovery is that bacteria also "talk" to each other by an ion channel dependent biofilm communication network that is necessary for their survival. This has raised the possibility that ion channel blocking drugs could constitute a new generation of antibacterials that are less susceptible to resistance. So indeed single molecule biology could contribute to study of several grand challenges in society. In each case, a limiting factor is currently the time required to study the large datasets generated by these molecules. *How can we help?*We will create a simple to use AI-based analysis application to allow rapid analysis of these data. These will be especially useful to industrial partners who produce large data sets during drug development but have few tools available to analyze this fully.

* 我们该怎么办 *开发和分发“人工智能”应用程序，使其他科学家能够分析现有方法难以分析的一类数据（“单分子数据”）。* 机器可以学习，但需要大量的训练 * 机器可以使用人工智能（“AI”）识别从亚马逊的“Alexa”到呼叫中心的设备中的语音。举个例子，只要问Alexa“AI”是什么，她就会告诉你。直到最近，随着计算机变得足够强大，这才成为可能。这些技术被统称为“机器学习”。这种“机器”的一个优点是，它们可以在没有人监督的情况下回答问题。一个限制是，它们首先需要大量的标记数据集来“学习”。这被称为“训练数据”。我们设计了一个“技巧”来生成大量的数据集，通过将模拟数据播放到记录设备中，然后记录回产生的信号。因为我们控制着整个过程，所以数据本身就以训练智能机器所必需的方式被“标记”。通过这项技术，再加上使用Google Brain免费提供的“TensorFlow”AI库，我们可以创建为我们分析数据的应用程序。为什么是“单分子”* 在动物细胞中发现的许多分子起着开关的作用。它们各自的“开”或“关”状态可以被测量为光脉冲或电流脉冲，从而提供实时的机械洞察力。它们在整个生物学中都很重要，估计仅针对这些分子开关（离子通道）的一个家族的药物的全球市场就为115亿美元。另一方面，测量这些“开关”的实验会产生很大的数据集，分析起来既困难又费力。单分子生物学能否有助于应对老年疾病、气候变化、抗细菌耐药性和全球恐怖主义？总之“是”：离子通道功能障碍，特别是负责许多与年龄有关的疾病。制药公司的兴趣是巨大的，因为它们是从镇静剂到心脏病药物的许多药物的目标。某些杀虫剂通过它们的离子通道起作用，但这些对人也有毒。其中一种毒剂，神经毒素“VX”，在暗杀金正男时被使用。即使是相对安全的驱蚊剂香茅也可以通过激活离子通道来驱蚊。对氯菊酯有抗药性的蚊子之所以有抗药性，是因为它们的离子通道发生了特定的突变，这给疟疾控制带来了真实的问题。植物也表达了一系列离子通道，这些离子通道具有关键作用，包括盐调节。由于气候变化正在增加许多农业地区的盐渍化，人们对根部离子通道的生物修饰是否可以促进作物在富盐土壤中的存活产生了浓厚的兴趣。离子通道也在合成生物学中被研究，因为它们可以被浓度远低于其他传感器的化学物质激活。已经提出了许多用途，例如探测爆炸物、生物武器、麻醉品或某些疾病。最近的一项发现是，细菌还通过依赖于离子通道的生物膜通信网络彼此“交谈”，这是它们生存所必需的。这增加了离子通道阻断药物可能成为新一代不易产生耐药性的抗菌药物的可能性。因此，单分子生物学确实可以为研究社会中的几个重大挑战做出贡献。在每种情况下，一个限制因素是目前研究这些分子产生的大型数据集所需的时间。* 我们能帮什么忙 *我们将创建一个简单易用的基于AI的分析应用程序，以快速分析这些数据。这些对于在药物开发期间产生大量数据集但几乎没有工具可用于全面分析的工业合作伙伴特别有用。

项目成果

CVS role of TRPV: from single channels to HRV assessment

TRPV 的 CVS 作用：从单一通道到 HRV 评估

• 发表时间: 2018
• 期刊: FASEB JOURNAL
• 影响因子: 4.8
• 作者: O'Brien Fiona

DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.

• DOI: 10.1371/journal.pone.0267452
• 发表时间: 2022
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ball STM;Celik N;Sayari E;Abdul Kadir L;O'Brien F;Barrett-Jolley R
• 通讯作者: Barrett-Jolley R

Comparison of Deep Learning Models for Fully Automated Single Channel Idealization

全自动单通道理想化深度学习模型的比较

• 发表时间: 2021
• 期刊: BIOPHYSICAL JOURNAL
• 影响因子: 3.4
• 作者: Ball Sam

Discriminant Analysis of Principle Component analyses of Physiological Data

生理数据主成分分析的判别分析

• DOI: 10.1101/2020.01.09.899898
• 发表时间: 2020
• 作者: Haidar O

Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data

Deep-Channel 使用深度神经网络从膜片钳数据中检测单分子事件

• DOI: 10.1101/767418
• 发表时间: 2019
• 作者: Celik N