Rule-based network optimization to infer dysregulated signaling from -omics data

基于规则的网络优化，从组学数据推断失调的信号传导

• 批准号: 9759666
• 负责人: Rohith Palli
• 金额: $ 4.4万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-05 至 2020-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759666
• 关键词: Algorithms; Atherosclerosis; B-Lymphocytes; Biological; Biological Models; Blood Platelets; Cells; Choriocarcinoma; Computer Simulation; Computer software; Data; Data Set; Development; Disease; Drug Targeting; Drug usage; FDA approved; Gene Proteins; Genetic Programming; HIV; Heterogeneity; High Prevalence; Human; Hypoxia; In Vitro; Investigation; Knowledge; Learning; Length; Logic; Methodology; Methods; Modeling; Molecular; Network-based; Pathologic; Pathway Analysis; Pathway interactions; Pharmaceutical Preparations; Placental Biology; Plant Roots; Platelet Activation; Property; Protein Tyrosine Phosphatase; Proteomics; Public Domains; RNA Interference; Research Personnel; Scientist; Signal Pathway; Signal Transduction; Signaling Molecule; System; Techniques; Therapeutic; Therapeutic Agents; Training; Translations; Update; Validation; base; cell motility; cytotrophoblast; drug efficacy; dynamic system; experimental study; high throughput screening; improved; in vivo; informatics tool; innovation; insight; kidney cell; knock-down; monocyte; network models; open source; pathway tools; simulation; therapeutic candidate; tool development; transcriptomics; trophoblast

Pathway analyses of omic data provide mechanistic insights which facilitate interpretation. Current pathway analysis approaches, however, are unable to distinguish between pathways which have divergent signal origin but common effector molecules because solutions are exclusively based on static properties. Sequential dynamical systems (SDS) modeling allows inference of dynamics in pathway analysis. Further, by capturing emergent phenomena in molecular networks, dynamic approaches to drug re-purposing facilitate in silico experimentation and investigation of non-target effects. A key hindrance to use of SDS models with omic data has been modeling variance within omic data as arising from intracellular stochasticity rather than cellular heterogeneity. To this end, I will develop methodology that accounts for heterogenous cell states in bulk omics data, and re-implement extant inference techniques to recover necessary and sufficient conditions for underlying network transitions. This will be accomplished by implementing Boolean update models, which take molecules as either active or inactive, across an ensemble of starting states to construct Ensemble Boolean Networks (EBN). EBNs will improve dynamic simulations of molecular networks and in-silico perturbation analysis. Specifically, EBN algorithms will then be applied in parallel with existing SDS algorithms to perform network-based pathway analysis of omics data and to investigate dysregulated signaling subnetworks in disease states for drug re-purposing. An SDS-based pathway-level metric that explicitly considers interactions between molecules will be achieved by perturbation analysis of pathway components followed by development of a pathway-level score based on a weighted node-level metric. I will use this technique to help our collaborators gain insight into placental biology and B cell migration using transcriptomic and proteomic datasets, respectively. An SDS- based algorithm to repurpose FDA approved drugs using omic data from drug-treated and disease-perturbed states will be assembled by quantifying signaling dysregulation in disease states from transcriptomic data in public domain. This technique will be applied to understand dysregulation of platelets and monocytes in the development of atherosclerosis in people living with HIV. SDS-based pathway analysis will improve the prediction of key nodes in pathways, facilitating translation of omic data into in vivo and in vitro studies. SDS- based repurposing will provide a powerful new way to combine prior knowledge, extant drug omic data, and extant disease omic data to uncover new potential therapeutic agents. Taken together, this proposal will develop a new technique called EBN and will apply it alongside other SDS-based techniques to generate innovative algorithms to retrieve key features and their regulatory context from omic datasets.

组学数据的路径分析提供了有助于解释的机制见解。当前途径 然而，分析方法无法区分具有不同信号来源的途径 但常见的效应分子，因为解决方案完全基于静态特性。顺序 动力系统（SDS）建模允许在路径分析中推断动力学。进一步地，通过捕获 分子网络中的新兴现象、药物再利用的动态方法在计算机中促进 非目标效应的实验和研究。 将 SDS 模型与组学数据一起使用的一个主要障碍是对组学数据内的方差进行建模，如下所示 由细胞内随机性而不是细胞异质性引起。为此，我将制定方法论 解释大量组学数据中的异质细胞状态，并重新实现现有的推理技术 恢复底层网络转换的充分必要条件。这将通过以下方式完成 实施布尔更新模型，该模型将整个整体中的分子视为活动或非活动 构建集成布尔网络（EBN）的起始状态。 EBN 将改善动态模拟 分子网络和计算机微扰分析。具体来说，EBN 算法将应用于 与现有的 SDS 算法并行，对组学数据执行基于网络的路径分析，并 研究疾病状态下失调的信号子网络以重新利用药物。 明确考虑分子之间相互作用的基于 SDS 的通路水平指标将是 通过对通路成分进行扰动分析，然后制定通路水平分数来实现 基于加权节点级指标。我将使用这种技术来帮助我们的合作者深入了解 分别使用转录组和蛋白质组数据集进行胎盘生物学和 B 细胞迁移。 SDS- 基于算法，​​使用来自药物治疗和疾病干扰的组学数据来重新利用 FDA 批准的药物 将通过根据转录组数据量化疾病状态中的信号失调来组装状态 公共领域。该技术将用于了解血小板和单核细胞的失调 艾滋病毒感染者动脉粥样硬化的发展。基于 SDS 的路径分析将改善 预测通路中的关键节点，促进组学数据转化为体内和体外研究。 SDS- 基于的重新利用将提供一种强大的新方法来结合先验知识、现有药物组学数据和 现有疾病组学数据以发现新的潜在治疗药物。 总而言之，该提案将开发一种称为 EBN 的新技术，并将其与其他技术一起应用 基于 SDS 的技术可生成创新算法来检索关键特征及其监管背景 来自组学数据集。