Computational Inference of Regulatory Network Dynamics on Cell Lineages

细胞谱系调控网络动力学的计算推断

基本信息

批准号：
9979901
负责人：
Sushmita Roy
金额：
$ 30.32万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-16 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9979901
关键词：
3-Dimensional Address Algorithms Binding Biological Process Cell Differentiation process Cell Line Cell Lineage Cell model Cell physiology Cells Chromatin Complex Computing Methodologies Correlation Studies DNA Data Data Set Dependence Development Disease Disease model Distal Epigenetic Process Gene Expression Gene Expression Regulation Generations Genes Genetic Genetic Transcription Genome Genomics Health Histones Human Maintenance Mammalian Cell Measures Messenger RNA Methods Modeling Output Patients Play Post-Translational Protein Processing Process Regenerative Medicine Regulation Regulator Genes Research Personnel Resources Role Sample Size Sampling Signaling Protein Software Tools Specific qualifier value Structure System Testing Training Uncertainty Update Validation cell fate specification cell type chromatin remodeling computerized tools epigenome experimental study genetic regulatory protein genome-wide genomic data human disease human model innovation learning network learning strategy machine learning method multi-task learning novel predictive modeling predictive test programs promoter reconstruction supervised learning tool transcription factor transcriptome

项目摘要

Regulatory networks that control which genes are expressed when, are critical players in the maintenance and transitions of different cell states. In mammalian systems such networks are established by a complex interplay of thousands of regulatory proteins such as transcription factors, chromatin remodelers and signaling proteins, histone post-translational modifications and three-dimensional organization of the genome. Hence, the identification of genome-scale regulatory networks and their changes remains a computational and experimental challenge, especially for rare and novel cell types. Through recent efforts of consortia projects we now have rich datasets measuring multiple components of the regulation machinery in model cell lines. These data enable the creation of a more complete regulatory network for these cell lines. Can we use this information to identify networks in new cell types where measuring only a few components of the regulation machinery is possible (e.g. the transcriptome)? Can we leverage more complete regulatory networks to predict new cell types, and to predict the effect of network perturbations to cellular state? To tackle these questions, in this proposal we will develop innovative network reconstruction methods to identify regulatory networks in novel and rare cell types by leveraging their relationships to well-studied cell types, as well as to each other. Our methods will use the framework of non-stationary graphical models to represent cell type-specific regulatory networks and will use multi-task learning to incorporate shared information between cell types in a lineage. Methods in Aim 1 will infer modular gene regulatory networks for each cell type and additionally refine an existing incomplete or uncertain lineage structure. Methods in Aim 2 will identify cell type-specific directed dependencies among chromatin state and transcription factors and how they impact target gene expression through proximal and long-range regulation. Our methods will be applied to two cell-fate specification problems: cellular reprogramming and multi-cell lineage forward differentiation. In cellular reprogramming, regulators and subnetworks hindering reprogramming efficiency will be predicted and tested using genetic perturbation experiments. In forward differentiation, regulatory network changes that drive alternate lineages will be identified and tested. Successful completion of this project will provide two broadly applicable software tools that will enable researchers to (i) accurately identify regulatory networks and their changes between different cell states in complex cell lineages, (ii) examine interactions among multiple levels of regulation and their impact on cell type-specific gene expression, and (iii) efficiently identify the most upstream regulatory genes and subnetworks that change cellular states. Software tools from this project will be made available and will be broadly applicable to diverse types of dynamic biological processes in development and disease.

控制哪些基因在维护和维护中的关键参与者时表达哪些基因的监管网络不同细胞状态的过渡。在哺乳动物系统中，这些网络由复杂的相互作用建立数千种调节蛋白，例如转录因子，染色质重塑剂和信号蛋白，组蛋白的翻译后修饰和基因组的三维组织。因此，基因组规模的监管网络及其变化的识别仍然是计算和实验挑战，尤其是针对稀有和新型细胞类型的挑战。通过财团的最新努力我们现在拥有丰富数据集的项目，测量模型单元格中规定机械的多个组件线。这些数据使创建这些细胞系的更完整的调节网络。我们可以使用吗？这些信息以识别新单元类型中的网络，其中仅测量了几个组件调节机械是可能的（例如转录组）吗？我们可以利用更完整的监管网络预测新的细胞类型，并预测网络扰动对细胞状态的影响？解决这些问题，在此提案中，我们将开发创新的网络重建方法来识别通过利用其与良好细胞的关系，新颖和稀有细胞类型中的调节网络类型以及彼此。我们的方法将使用非平台图形模型的框架表示特定于细胞类型的调节网络，并将使用多任务学习来合并共享谱系中的细胞类型之间的信息。 AIM 1中的方法将推断模块化基因调节网络每种细胞类型，并进一步完善现有的不完整或不确定的谱系结构。 AIM 2中的方法将确定染色质状态和转录因子之间的细胞类型特异性定向依赖性以及如何它们通过近端和远距离调节影响靶基因表达。我们的方法将应用于两个细胞命令规范问题：细胞重编程和多细胞谱系向前分化。在将预测细胞重编程，调节剂和子网阻碍重编程效率的，并且将被预测使用遗传扰动实验进行了测试。在向前区分中，监管网络变化驱动将确定和测试替代血统。成功完成该项目将为两个广泛提供两个适用的软件工具将使研究人员能够（i）准确确定监管网络及其复杂细胞谱系中不同细胞状态之间的变化，（ii）检查多个级别之间的相互作用调节及其对细胞类型特异性基因表达的影响，并且（iii）有效识别最多改变细胞状态的上游调节基因和子网。该项目的软件工具将是可用，并将广泛适用于开发中各种类型的动态生物学过程和疾病。