利用时间数据从系统层面重建调节内皮屏障完整性的激酶磷酸信号网络

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-11-16 DOI:10.1038/s41540-024-00468-9

Ling Wei, John D Aitchison, Alexis Kaushansky, Fred D Mast

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引用次数: 0

摘要

磷酸信号网络控制着细胞过程。我们利用两种计算策略构建了由凝血酶刺激脑内皮细胞引起的激酶介导的调控网络：时间通路合成器（TPS）使用磷酸蛋白组学数据作为输入，而时间解析激酶网络生成器（TREKING）则使用激酶抑制剂筛选。TPS 和 TREKING 预测了具有独特网络拓扑结构的重叠屏障调控激酶。每种策略都能有效描述调控信号网络，并广泛适用于各种生物系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproteomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.