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？ *机器可以学习，但是他们需要大量的培训*机器可以识别亚马逊“ Alexa”设备中的语音，以使用人工智能（“ AI”）呼叫中心。例如，请问Alexa是什么“ AI”，她会告诉您。由于计算机变得足够强大，这才是最近才有可能的。这样做的技术集体称为“机器学习”。这种“机器”的优点是，他们可以回答人们无人看出的问题。一个限制是，他们首先需要大量标记的数据集“学习”。这称为“培训数据”。我们已经设计了一个“技巧”来生成大量数据集，通过播放模拟数据录制设备，然后录制结果信号。因为我们控制了整个过程，所以数据本质上是按照训练智能机器所需的方式“标记”的。通过这种技术，以及使用Google Brain免费获得的“ Tensorflow” AI库的使用，我们可以创建为我们分析数据的应用程序。然后可以将他们的个体“在”或“关闭”状态作为光或电流的脉冲来测量实时机械洞察力。它们在整个生物学上都很重要，仅针对这些分子开关（ION渠道）的药物的估计全球市场（ION渠道）为115亿美元。另一方面，测量这些“开关”的实验产生了很难分析的大数据集。 Pharma的兴趣是巨大的，因为它们是从镇静剂到心脏药物的许多药物的目标。某些杀虫剂通过其离子通道起作用，但它们也对人有毒。一位这样的经纪人，神经毒素“ VX”被用来被暗杀时的新闻。即使是相对安全的驱虫剂香龙，也通过激活离子通道排斥蚊子。抗氯菊酯的蚊子具有抵抗力，因为它们在离子通道中具有特定的突变，从而在疟疾控制中产生了真正的问题。植物也表达一系列具有关键作用在内的离子通道，包括盐调节。由于气候变化正在增加许多农业地区的盐度，因此对根离子通道的生物修饰是否可以促进农作物在富含盐的土壤中的生存感，这引起了人们的兴趣。还研究了合成生物学的离子通道，因为它们的浓度远低于其他传感器的化学物质可以激活它们。已经提出了许多用途，例如发现炸药，生物武器，麻醉品或某些疾病。最近的发现是，细菌还通过离子渠道依赖的生物膜通信网络互相“交谈”，这是其生存所必需的。这增加了离子通道阻断药物可能构成新一代的抗菌物质的可能性，这些抗菌物质不太容易受到抗性的影响。因此，确实，单分子生物学可以为研究社会的几个巨大挑战做出贡献。在每种情况下，限制因素当前是研究这些分子产生的大数据集所需的时间。 *我们如何提供帮助？*我们将创建一个易于使用基于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过程中生产大量数据但几乎没有可用的工具来完全分析的工业伙伴对工业伙伴特别有用。

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