Machine learning of time-series single-cell drug screening to elucidate HIV latency control mechanisms

时间序列单细胞药物筛选的机器学习阐明 HIV 潜伏期控制机制

基本信息

批准号：
10674721
负责人：
Diwakar Shukla
金额：
$ 18.12万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10674721
关键词：
Affect Area Biochemistry Biological Biology Biomedical Engineering Biophysics Career Transition Award Cells Chemical Agents Chemical Models Chemical Structure Chemicals Clinical Communities Data Data Set Decision Making Disease Drug Compounding Drug Screening Drug Targeting Engineering Etiology Excision FDA approved Faculty Feedback Fluorescence Microscopy Foundations Funding Gene Expression Genes Goals HIV Health Hour Human Image Learning Libraries Literature Machine Learning Maps Microscopy Mission Modeling National Institute of Allergy and Infectious Disease Noise Outcome Pharmaceutical Preparations Pharmacologic Substance Pharmacotherapy Publishing Research Research Assistant Science Series Shock Structure Structure-Activity Relationship Systems Biology T-Lymphocyte Therapeutic Time Training United States National Institutes of Health Validation Virus Work autoencoder computational pipelines computer science deep learning deep neural network design drug development drug discovery drug repurposing experimental study generative adversarial network in silico insight integration site interest latent HIV reservoir learning strategy machine learning pipeline mathematical sciences member novel novel therapeutics protein expression reactivation from latency response small molecule small molecule libraries success time use tool treatment strategy virology

项目摘要

PROJECT SUMMARY Gene expression dynamics yield a wealth of insight into the underlying structure-function relationships of gene circuitry and the blueprints of disease. This is especially true for viruses whose decision-making is highly dependent on their gene expression dynamics. Time-series gene expression perturbation data elucidates biological causality and is essential to identify biological mechanisms, perform chemical biology analyses, and for the discovery of novel drugs. However, a pipeline to comprehensively and effectively analyze such time-series perturbation data does not exist. In this work, we propose to apply machine learning to unravel the hidden (latent) structure of human immunodeficiency virus (HIV) time- series gene expression when affected by chemical perturbations. This highly interdisciplinary effort includes scientific areas relevant to the mission of the NIH such as biological, clinical, physical, chemical, computational, engineering, and mathematical sciences. The proposed areas of research combine machine learning, virology, systems biology, chemical sciences, single-cell biophysics, and pharmaceutical sciences. The research will train and support two faculty members and two graduate research assistants for the two-year term.

项目概要基因表达动态可以深入了解潜在的结构-功能关系基因线路和疾病蓝图。对于病毒来说尤其如此，其决策高度依赖于它们的基因表达动态。时间序列基因表达扰动数据阐明生物因果关系，对于识别生物机制、进行化学分析至关重要生物学分析以及新药的发现。然而，全面和有效分析此类时间序列扰动数据的方法并不存在。在这项工作中，我们建议应用机器学习揭开人类免疫缺陷病毒（HIV）隐藏（潜在）结构的时间- 受化学扰动影响时的一系列基因表达。这项高度跨学科的努力包括与 NIH 使命相关的科学领域，例如生物学、临床、物理、化学、计算、工程和数学科学。拟议的研究领域结合机器学习、病毒学、系统生物学、化学科学、单细胞生物物理学和制药科学。该研究将培训和支持两名教员和两名研究生研究助理，任期两年。