Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks.
Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks.
复制标题
整合知识和组学，通过信号网络的大规模模型破译机制。
DOI：
10.15252/msb.202211036
复制
发表时间：
2022-07
影响因子：
9.9
通讯作者：
中科院分区：
文献类型：
--
作者：
信号转导控制细胞行为，其失调往往导致人类疾病。为了理解这一过程，我们可以使用基于先验知识的网络模型，其中节点代表生物分子，通常是蛋白质，而边表示它们之间的相互作用。几种计算方法将联合收割机非靶向组学数据与先验知识相结合，以估计特定生物场景中信号网络的状态。在这里，我们回顾，比较和分类最近的网络方法，根据其输入组学数据，先验知识和基础方法的特点。我们强调了该领域现有的挑战，例如普遍缺乏基础事实和先验知识的局限性。我们还指出了可能产生深远影响的新的组学发展，例如单细胞蛋白质组学或蛋白质构象变化的大规模分析。我们为有兴趣的用户提供了一个介绍，寻求大规模研究细胞信号传导的策略，并为经验丰富的建模人员提供了更新。 基于先验知识的网络模型用于理解信号转导。本文根据输入组学数据、先验知识和基本方法的特点，对最近的网络方法进行了比较和分类。
Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single‐cell proteomics or large‐scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers. Network models based on prior knowledge are used to understand signal transduction. This Review compares and classifies recent network approaches according to their characteristics in terms of input omics data, prior knowledge, and underlying methodologies.
影响因子：
4.3
作者：
Haynes WA;Higdon R;Stanberry L;Collins D;Kolker E
通讯作者：
Kolker E
影响因子：
14.9
作者：
Judeh T;Johnson C;Kumar A;Zhu D
通讯作者：
Zhu D
影响因子：
--
作者：
Vogt T;Czauderna T;Schreiber F
通讯作者：
Schreiber F