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.

控制哪些基因何时表达的调控网络是维持和维持基因的关键参与者。不同细胞状态的转变。在哺乳动物系统中，这种网络是通过复杂的相互作用建立的数千种调节蛋白，如转录因子、染色质重塑剂和信号蛋白，组蛋白翻译后修饰和基因组的三维组织。因此，基因组规模调控网络及其变化的识别仍然是一个计算和实验挑战，特别是对于稀有和新颖的细胞类型。通过近期财团的努力我们现在拥有丰富的项目数据集，可以测量模型细胞中调节机制的多个组件线。这些数据使得能够为这些细胞系创建更完整的调控网络。我们可以使用这些信息可用于识别新细胞类型中的网络，其中仅测量细胞的少数组成部分调节机制是否可能（例如转录组）？我们能否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？为了解决这些问题，在本提案中，我们将开发创新的网络重建方法来识别通过利用它们与经过充分研究的细胞的关系，在新颖和稀有细胞类型中建立调控网络类型，以及彼此之间。我们的方法将使用非平稳图形模型的框架来代表细胞类型特异性的调控网络，并将使用多任务学习来整合共享的谱系中细胞类型之间的信息。目标 1 中的方法将推断模块化基因调控网络每种细胞类型，并进一步完善现有的不完整或不确定的谱系结构。目标 2 中的方法将识别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程调节影响靶基因表达。我们的方法将应用于两个细胞命运规范问题：细胞重编程和多细胞谱系向前分化。在细胞重编程、调节器和子网络阻碍重编程效率将被预测和使用遗传扰动实验进行测试。在前向分化中，监管网络的变化推动替代谱系将被识别和测试。该项目的成功完成将提供两个广泛的适用的软件工具将使研究人员能够（i）准确识别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）检查多个水平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，以及（iii）有效地识别最重要的改变细胞状态的上游调控基因和子网络。该项目的软件工具将是可用并将广泛适用于开发中的各种类型的动态生物过程和疾病。