CAREER: Inference in temporal signaling and transcriptional data

大多数高通量生物学实验的目标是产生关于基因、蛋白质和其他参与生物过程的实体如何控制细胞功能和细胞表型的其他方面的特定假设。时间序列算法已被证明是一种强大的计算策略，通过使用事件的顺序来帮助识别调节相互作用，从基因表达数据中理解转录调节。然而，从大量基因表达数据中推断出的相互作用的质量和类型是有限的，这些数据是在许多不同的细胞中平均的，不能反映蛋白质的翻译后修饰。单细胞RNA-seq测量单个细胞中的基因表达，通过生物过程捕获不同的细胞状态和细粒度进展。基于质谱的磷酸化蛋白质组学数据揭示了在细胞信号反应过程中随着时间的推移而发生的蛋白质磷酸化的快速和广泛的变化。这两种类型的数据对于发现特定条件的转录调控和信号传导机制具有很大的潜力。该项目将设计新颖的时间序列算法，以改进转录和信号网络相互作用的预测。这些方法将作为开源软件开发，以便计算方法可以在各种各样的生物系统中采用。时间序列数据分析也将被纳入当地的公共推广计划和新的培训研讨会，旨在向生物学学生教授计算思维。生物过程是动态的，单细胞基因表达数据可以提供时间行为的嘈杂快照。现有的计算技术可以通过单细胞数据的时间过程来估计每个细胞的进展，从而产生细胞特异性的伪时间。这些伪时间序列数据是不精确和不规则间隔的，但比传统的时间序列实验包含更多的时间点。该项目将使用因果分析技术，需要大量的时间点来推断因果基因-基因调控关系。核方法将调整因果关系算法，以适应单细胞数据的异质性和转录调控的组合性质。为了研究细胞信号，对时间序列磷酸化变化的计算分析不仅可以确定哪些蛋白质在信号响应中是活跃的，还可以确定它们参与的时间间隔。该项目将评估非平稳高斯过程和隐马尔可夫模型，以模拟蛋白质磷酸化的时间变化。然后，一种新的信号通路重建算法将灵活地整合多种类型的数据派生约束（例如，早期响应者不能被后期响应者激活，蛋白质-蛋白质相互作用网络连接关系等），使用统计关系学习技术进行概率推理。有关该项目的更多信息，请访问https://www.biostat.wisc.edu/~gitter/。

The goal of most high-throughput biological experiments is to generate specific hypotheses about how genes, proteins, and other entities involved in a biological process control cellular functions and other aspects of cellular phenotypes. Time series algorithms have proven to be a powerful computational strategy for understanding transcriptional regulation from gene expression data by using the ordering of events to help identify regulatory interactions. However, there are limits to the quality and types of interactions that can be inferred from bulk gene expression data, which are averaged over many different cells and do not reflect protein post-translational modifications. Single-cell RNA-seq measures gene expression in individual cells, capturing diverse cellular states and fine-grained progression through biological processes. Mass spectrometry-based phosphoproteomics data reveal the rapid and widespread changes in protein phosphorylation that occur over time during cellular signaling responses. These two types of data have great potential for discovering condition-specific transcriptional regulatory and signaling mechanisms. This project will design novel time series algorithms to improve the prediction of transcriptional and signaling network interactions. These methods will be developed as open source software so that the computational approaches can be adopted in a wide variety of biological systems. Time series data analysis will also be incorporated into local public outreach programs and new training workshops designed to teach computational thinking to biology students.Biological processes are dynamic, and single-cell gene expression data can provide noisy snapshots of temporal behaviors. Existing computational techniques can estimate each cell's progression through a temporal process from single-cell data, producing cell-specific pseudo-times. These pseudo-time series data are imprecise and irregularly-spaced but contain far more time points than a traditional time series experiment. This project will use causality analysis techniques that require a large number of time points to infer causal gene-gene regulatory relationships. Kernel methods will adapt the causality algorithms to accommodate the heterogeneity of single-cell data and combinatorial nature of transcriptional regulation. To study cellular signaling, computational analysis of time series phosphorylation changes will determine not only which proteins are active in a signaling response but also the temporal intervals in which they are involved. The project will assess both non-stationary Gaussian processes and hidden Markov models for modeling temporal changes in protein phosphorylation. A novel signaling pathway reconstruction algorithm will then flexibly integrate many types of data-derived constraints (for example, early responders cannot be activated by late responders, protein-protein interaction network connectivity relationships, etc.) using statistical relational learning techniques for probabilistic reasoning. Additional information about the project will be available at https://www.biostat.wisc.edu/~gitter/.