An integrative, data-driven, and computational approach to uncovering dynamic mechanisms of early viral infection

一种揭示早期病毒感染动态机制的综合、数据驱动和计算方法

• 批准号: 10276969
• 负责人: David van Dijk
• 金额: $ 41.88万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276969
• 关键词: Algorithms; Animal Model; Atlases; Biological; Biological Models; Biological Process; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communicable Diseases; Communities; Complex; Computer Analysis; Computing Methodologies; Coupling; Data; Data Analyses; Data Collection; Disease; Disease Outcome; Environment; Family; Future; Genes; Guidelines; Human; Immune response; In Vitro; Infection; Learning; Logic; Machine Learning; Methods; Modality; Organism; Organoids; Phase; Physiological; Predisposition; Process; Regulator Genes; Research; Resources; Severe Acute Respiratory Syndrome; System; Technology; Therapeutic; Therapeutic Intervention; Tissues; Training; Virus; Virus Diseases; biological systems; causal variant; combinatorial; computerized tools; data integration; design; gene interaction; in vivo; innovation; insight; large datasets; machine learning algorithm; machine learning method; mouse model; novel; novel strategies; pathogen; predictive modeling; single cell analysis; single cell technology; targeted treatment

Project Summary Cutting-edge technologies are generating large datasets across biological processes, including those following viral infection and host responses. However, lack of computational tools that can extract meaningful insights, and lack of ability to integrate information across different model systems and data modalities, are roadblocks to deriving biological and mechanistic understanding of these processes. The recent rise of devastating viruses including SARS family viruses reveals that a deeper, basic mechanistic understanding of viral infection is still lacking. Specifically, new insights into early viral infection (asymptomatic replication phase) and early-responding genes that govern infection and disease outcome are critical for understanding progression of infection and host responses. During my postdoctoral research, I developed several widely-used algorithms for biomedical machine learning and single-cell data analysis, and applied these to a broad range of biological systems, including infectious disease. Here, I propose to develop a completely new approach that is founded in cross-modal computational analysis and can be applied to dynamic processes across living systems. In this proposal, the method will be trained upon and applied to uncovering virus infection dynamics. By leveraging single-cell technologies, combinatorial CRISPR perturbation, and advanced machine learning, this new approach will learn the gene regulatory logic that governs infection. By spanning model systems, I will extract information that can be derived more cleanly from in-vitro systems, such as early infection timepoints. Through cross-integration of these data with in-vivo data from mouse models we will bring the precision questions that can be asked in human organoids together with the physiological environment of animal models, powering our ability to derive relevant insights into gene networks underlying a complex, dynamic process. I will build a single-cell atlas of virus infection and train a machine learning algorithm to obtain a predictive model of infection dynamics. By also integrating data from single-cell combinatorial CRISPR perturbation, I will infer causal gene networks as well as synergistic gene interactions that govern infection dynamics. This combination of advanced machine learning methods, large-scale single-cell analysis, and gene perturbation data will allow discovery of the drivers of infection, signatures of both susceptibility and protection, and gene networks that can ultimately be targeted for therapeutic intervention. Synergistic gene interactions will open up future paths to potentially more effective, specific, and even combinatorial therapies. The innovative coupling of computational methods and deep data collection to extract information, particularly during early infection phases, has the potential to fundamentally change our understanding of viral infections, as well as provide a framework that can be applied to a broad range of biological processes and diseases to obtain deep mechanistic understanding.

项目摘要 尖端技术正在生成跨生物过程的大型数据集，包括以下过程 病毒感染和宿主反应。然而，由于缺乏能够提取有意义见解的计算工具， 以及缺乏跨不同模型系统和数据模态集成信息的能力，是 从生物学和机械学的角度理解这些过程。最近出现的毁灭性病毒 包括SARS家族病毒的研究表明，对病毒感染的更深层次的、基本的机制理解仍然是一个问题。 缺乏具体而言，对早期病毒感染（无症状复制期）和早期应答的新见解 控制感染和疾病结果的基因对于理解感染和宿主的进展至关重要。 应答在我的博士后研究期间，我开发了几种广泛使用的生物医学机器算法 学习和单细胞数据分析，并将其应用于广泛的生物系统，包括 传染病在这里，我建议开发一种全新的方法，建立在跨模态 计算分析，并可应用于整个生命系统的动态过程。在本提案中， 方法将被训练并应用于揭示病毒感染动态。通过利用单细胞 技术，组合CRISPR扰动和先进的机器学习，这种新方法将学习 控制感染的基因调控逻辑通过跨越模型系统，我将提取信息， 更干净地从体外系统中获得，例如早期感染时间点。通过交叉整合， 这些数据与来自小鼠模型的体内数据一起，我们将带来可以在人类中提出的精确性问题。 类器官与动物模型的生理环境一起，使我们能够获得相关的 深入了解基因网络背后的复杂动态过程。我会建立一个病毒感染的单细胞图谱 并训练机器学习算法以获得感染动态的预测模型。通过整合 根据单细胞组合CRISPR扰动的数据，我将推断因果基因网络以及协同基因网络。 控制感染动力学的基因相互作用。这种先进的机器学习方法的结合， 大规模的单细胞分析和基因扰动数据将允许发现感染的驱动因素， 敏感性和保护性的特征，以及最终可以靶向治疗的基因网络。 干预协同基因相互作用将开辟未来的道路，潜在的更有效，具体， 甚至是组合疗法计算方法和深度数据收集的创新结合， 提取信息，特别是在感染早期阶段，有可能从根本上改变我们的生活。 了解病毒感染，以及提供一个框架，可以应用于广泛的生物学 过程和疾病，以获得深入的机械理解。