Learn Systems Biology Equations From Snapshot Single Cell Genomic Data

从快照单细胞基因组数据学习系统生物学方程

• 批准号: 10736507
• 负责人: Jianhua Xing
• 金额: $ 31.8万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10736507
• 关键词: ATAC-seq; Address; Algorithms; Benchmarking; Binding Sites; Biochemical; Biological Models; Biology; Birth; CRISPR interference; Cell Fate Control; Cell physiology; Cells; Cellular biology; Cessation of life; Communities; Complement; Complex; Computers; DNA Binding; DNA sequencing; Data; Data Analyses; Data Set; Development; Dimensions; Dose; Elements; Equation; Error Sources; Eukaryotic Cell; GATA1 gene; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Code; Genetic Transcription; Genomic approach; Genomics; Goals; Grant; Graph; Informatics; Knowledge; LMO2 gene; Label; Learning; Machine Learning; Mathematics; Measurement; Metabolic; Methods; Modality; Modeling; Mutation; Names; Nature; Pathologic Processes; Pattern; Physiological Processes; Procedures; Process; Publishing; RNA; RNA Splicing; Regulation; Research; Sampling; Stochastic Processes; Systems Biology; Systems Theory; TAL1 gene; Techniques; Testing; Time; Work; cofactor; combinatorial; computational pipelines; computer framework; computerized tools; data acquisition; data integration; data space; differential geometry; dynamic system; environmental change; experimental study; extracellular; gene regulatory network; genome-wide; genomic data; high dimensionality; improved; in silico; informatics tool; insight; mathematical model; molecular dynamics; multimodal data; predictive modeling; reconstruction; response; single cell analysis; statistics; success; tool; tool development; transcription factor; transcriptome; transcriptomics; vector

Understanding how cells respond to environmental changes is a fundamental task in systems biology and has profound biomedical implications. Mathematical modeling on small network motifs using dynamical systems theories has been successful on providing mechanistic insight and guidance, but generalization to a genome- wide intertwined gene regulatory network is challenging. Single cell genomics approaches emerge as powerful tools for studying cellular processes, but the destructive nature of most single cell techniques makes it unfeasible to extract dynamical information of cellular processes. In addition, a number of grand challenges impede further development of the field, such as trajectory inference, effect of various sources of errors on data analysis, and validating and benchmarking tools for single cell measurements and analyses. The goal of this proposed research is to tackle these challenges through integrating dynamical systems modeling into single cell genomics analyses. The proposed research is based on recent advances in the single cell genomics field that one can extract both transcriptome (x) and estimation of RNA velocity (i.e., instant time derivatives of transcriptome, dx/dt) from single cell genomics data. We further developed a unified theoretical framework that allows estimating the velocity information from various types of single cell data, and a machine learning based computational pipeline of reconstructing systems biology equations for genomewide regulatory networks, together with a computer package, dynamo, released to the community. This integration between single cell genomics analyses and systems biology modeling provides quantitative mechanistic and dynamics information. We propose to further develop our package and computational framework to address several limitations in our published work. In Aim 1, we will first develop dynamo to interface with other single cell analysis and dynamics modeling packages, and expand the types of single cell data to be analyzed. Then we will develop and test a discrete dynamical model for full stochastic cellular dynamics based on the graph representation of discrete vector fields. In Aim 2, we will first develop a systematic pipeline of integrating data of multi-modality (e.g., ATAC-seq, DNA sequencing and binding site analyses, etc) and dynamo to identify genetic codes of combinatorial function of transcriptional factors, the so-called composite elements in genetics. Eukaryotic cells use a combination of a finite number of transcription factors to generate a large number of different target gene regulation patterns. Cracking the genetic code at the genome-wide level is fundamental to cell biology but challenging despite extensive efforts. Then we will expand the pipeline to reconstruct biology- informed systems biology models for the genomowide gene regulation. We will evaluate the in silico predictions from the model against several Perturb-seq datasets.

了解细胞如何响应环境变化是系统生物学的一项基本任务，并且 深远的生物医学意义。使用动力系统对小型网络主题进行数学建模 理论已经成功地提供了机制洞察和指导，但推广到基因组- 广泛交织的基因调控网络具有挑战性。单细胞基因组学方法变得强大 研究细胞过程的工具，但大多数单细胞技术的破坏性本质使其 无法提取细胞过程的动态信息。此外，还有许多重大挑战 阻碍该领域的进一步发展，例如轨迹推断、各种误差源对系统的影响 用于单细胞测量和分析的数据分析、验证和基准测试工具。目标是 这项拟议的研究旨在通过将动力系统建模集成到 单细胞基因组学分析。拟议的研究基于单细胞基因组学的最新进展 可以提取转录组 (x) 和估计 RNA 速度（即，即时时间导数）的领域 来自单细胞基因组数据的转录组，dx/dt）。我们进一步开发了一个统一的理论框架 允许从各种类型的单细胞数据估计速度信息，并且基于机器学习 为全基因组调控网络重建系统生物学方程的计算管道， 与计算机包 dynamo 一起发布到社区。单细胞之间的这种整合 基因组学分析和系统生物学建模提供定量机制和动力学 信息。我们建议进一步开发我们的包和计算框架来解决几个问题 我们发表的作品的局限性。在目标 1 中，我们将首先开发发电机与其他单细胞连接 分析和动力学建模软件包，并扩展了要分析的单细胞数据的类型。然后我们 将开发和测试基于该图的全随机细胞动力学的离散动力学模型 离散向量场的表示。在目标 2 中，我们将首先开发一个集成数据的系统化管道 多模态（例如 ATAC-seq、DNA 测序和结合位点分析等）和发电机来识别 转录因子组合功能的遗传密码，即遗传学中所谓的复合元件。 真核细胞使用有限数量的转录因子的组合来产生大量的 不同的靶基因调控模式。在全基因组水平上破解遗传密码是 尽管付出了巨大的努力，但细胞生物学仍具有挑战性。然后我们将扩大重建生物学的管道—— 全基因组基因调控的知情系统生物学模型。我们将通过计算机评估 模型针对多个 Perturb-seq 数据集的预测。