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生物过程中涉及的基因、蛋白质和其他实体如何控制细胞功能和细胞表型的其他方面的具体假设。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控相互作用，来理解基因表达数据中的转录调控。然而，从大量的基因表达数据中可以推断出的相互作用的质量和类型是有限的，这些数据是许多不同细胞的平均数据，不能反映蛋白质翻译后的修饰。单细胞RNA-SEQ测量单个细胞中的基因表达，通过生物过程捕捉不同的细胞状态和细粒度的进展。基于质谱学的磷蛋白质组学数据揭示了在细胞信号响应过程中，随着时间的推移，蛋白质磷酸化发生的快速和广泛的变化。这两种类型的数据在发现特定条件的转录调控和信号机制方面具有巨大的潜力。该项目将设计新的时间序列算法，以提高转录和信令网络相互作用的预测。这些方法将被开发为开放源码软件，以便计算方法可以在各种生物系统中采用。时间序列数据分析还将被纳入当地的公共推广计划和新的培训研讨会，旨在向生物学学生传授计算思维。生物过程是动态的，单细胞基因表达数据可以提供嘈杂的时间行为快照。现有的计算技术可以根据单细胞数据通过时间过程估计每个细胞的进程，从而产生特定于细胞的伪时间。这些伪时间序列数据不精确，间隔不规则，但包含的时间点比传统的时间序列实验多得多。这个项目将使用需要大量时间点的因果分析技术来推断因果基因-基因调控关系。核方法将调整因果算法，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将确定哪些蛋白质在信号反应中是活跃的，而且还将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。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，早期响应不能被延迟响应激活，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/.