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

基因组数据的路径分析提供了便于解释的机械性见解。电流通路 然而，分析方法不能区分具有不同信号来源的通路 而是常见的效应器分子，因为溶液完全基于静态属性。序贯 动力学系统(SDS)建模允许在通路分析中对动力学进行推断。此外，通过捕获 分子网络中的涌现现象，促进药物再利用的动态途径 非靶标效应的实验和研究。 将SDS模型与OMIC数据一起使用的一个关键障碍是将OMIC数据中的方差建模为 源于细胞内的随机性，而不是细胞的异质性。为此，我将制定方法论 这解释了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以 恢复底层网络过渡的充分必要条件。这将通过以下方式实现 实现布尔更新模型，该模型将分子视为活动的或非活动的 以构造集合布尔网络(EBN)。EBNS将改进动态模拟 分子网络和电子微扰分析。具体地说，EBN算法将应用于 与现有的SDS算法并行，以执行基于网络的组学数据路径分析并 研究疾病状态下药物再利用的失调信号子网络。 明确考虑分子间相互作用的基于十二烷基硫酸钠的路径水平度量将是 通过对路径成分进行扰动分析，然后开发路径级别分数来实现 基于加权的节点级度量。我将使用此技术帮助我们的合作者深入了解 胎盘生物学和B细胞迁移，分别使用转录和蛋白质组数据集。一份特别提款单- 利用药物治疗患者和疾病患者的基因组数据重新调整FDA批准的药物用途的算法 将通过从转录数据中量化疾病状态中的信号失调来组装状态 公有领域。这项技术将被应用于了解糖尿病患者血小板和单核细胞的失调。 艾滋病毒携带者动脉粥样硬化的发展。基于十二烷基硫酸钠的途径分析将改善 预测通路中的关键节点，促进将基因组数据转化为体内和体外研究。十二烷基硫酸钠- 基于用途的再利用将提供一种强大的新方法来结合先验知识、现有的药物组学数据和 现有的疾病基因组数据，以发现新的潜在治疗药物。 综上所述，这项提议将开发一种名为EBN的新技术，并将与其他 基于SDS的技术，用于生成用于检索关键特征及其监管背景的创新算法 来自基因组数据集。

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.