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：
10402668
负责人：
Diwakar Shukla
金额：
$ 22.13万
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/10402668
关键词：
Accounting 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 Lead Learning Libraries Literature Machine Learning 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 Training Support United States National Institutes of Health Validation Variant Virus Work autoencoder base 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.

项目摘要基因表达动力学对基础结构功能关系产生丰富的见识基因回路和疾病的蓝图。对于决策的病毒尤其如此高度依赖其基因表达动力学。时间序列基因表达扰动数据阐明生物因果关系，对于识别生物学机制，进行化学是必不可少的生物学分析，并发现新型药物。但是，一条综合的管道和有效分析此类时间序列扰动数据不存在。在这项工作中，我们建议申请机器学习以阐明人类免疫缺陷病毒（HIV）时间的隐藏（潜在）结构 - 串联基因表达受到化学扰动的影响。这项高度跨学科的努力包括与NIH的任务相关的科学领域，例如生物，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药科学。这项研究将培训和支持两名教职员工和两名毕业生两年期的研究助理。