Computational methods to predict gene regulatory network dynamics and cell state transitions

项目摘要 这项研究计划的目标是提供工具，发现转录网络，控制 细胞命运的决定由细胞状态转换驱动的细胞命运决定是从细胞分裂到细胞分裂的基本细胞过程的基础。 发展到细胞重编程。有机会利用公开的基因组数据 开发细胞状态转换动力学的预测计算模型。所提出的方法将提供 意味着深入了解细胞命运的决策，以及它是如何转录调控，给定特定的细胞， 命运决定点和适当的数据。这种决策点的例子包括控制表皮细胞的生长， 再生，或维持造血过程中骨髓细胞命运之间的平衡。为了弥合 基因组学和细胞动力学之间的差距，统计和计算建模的挑战必须是 克服两个关键的挑战形成了这项研究计划的基础：1）发展统计方法， 推断调控网络，同时考虑单个细胞之间的变异水平，以及2）开发 将细胞内的基因调控动力学和细胞间的细胞间通讯结合起来的计算模型 细胞为了解决第一个挑战，我们将开发机器学习模型来预测基因表达 时间序列数据的动态。这些模型将能够根据基因的时间模式对基因进行分类， 结果将为基因网络推断提供信息。然后，我们将开发网络推理的方法， 整合多模式数据（单细胞RNA和ATAC测序）以及细胞-细胞信号传导信息， 学习控制特定细胞状态转换的网络。为了应对第二个挑战，我们将开发 基于微分方程的多尺度模型的基因调控网络动力学耦合细胞- 外部信号动力学这将使我们能够捕捉分子和细胞动力学在高 分辨率，从而确定哪些参数对系统施加关键控制。我们将使用贝叶斯 用于参数推断的方法，以使模型适合数据并执行模型选择， 需要多尺度模型推理。模型将通过其应用于特定的 系统，包括细胞分化（例如，造血过程中的骨髓命运决定）和发育（例如， 肾单位祖细胞命运决定）。在每一个器官系统中，模型预测将被测试 通过合作进行实验。在迭代测试之后，将制作开源的、经过验证的方法。 广泛用于研究细胞命运决策的动态过程。

Project Summary The goal of this research program is to provide tools for the discovery of transcriptional networks that control cell fate decisions. Cell fate decisions driven by cell state transitions underlie essential cell processes from development to cellular reprogramming. There is an opportunity to make use of publicly available genomic data to develop predictive computational models of cell state transition dynamics. The methods proposed will offer means to gain insight into cell fate decision-making and how it is transcriptionally regulated, given specific cell fate decision points and suitable data. Examples of such decision points include control of epidermal regeneration, or the maintenance of balance among myeloid cell fates during hematopoiesis. In order to bridge the gap between genomics and cell dynamics, statistical and computational modeling challenges must be overcome. Two key challenges form the basis of this research program: 1) developing statistical methods to infer regulatory networks while accounting for the levels of variability between single cells, and 2) developing computational models to couple gene regulatory dynamics within cells and cell-cell communication between cells. To address the first challenge, we will develop machine learning models to predict gene expression dynamics from time-series data. These models will be able to classify genes by their temporal patterns, and the results will inform gene network inference. We will then develop methods for network inference that integrate muti-modal data (single-cell RNA and ATAC sequencing) as well as cell-cell signaling information to learn networks that control specific cell state transitions. To address the second challenge, we will develop differential equation-based multiscale models of the gene regulatory network dynamics coupled with the cell- external signaling dynamics. This will allow us to capture both molecular and cellular dynamics in high resolution, and thus identify which parameters exert key control over the system. We will use Bayesian methods for parameter inference to fit models to data and perform model selection, adapting methods where needed for multiscale model inference. Models will be rigorously evaluated through their application to specific systems, including cell differentiation (e.g. myeloid fate decisions during hematopoiesis) and development (e.g. nephron progenitor cell fate decisions). In each of these organ systems, models predictions will be tested experimentally via collaborations. Following iterative testing, open-source, validated methods will be made widely available for the study of the dynamic processes of cell fate decision-making.