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PLoS computational biology. 2025 Apr 4;21(4):e1012864. doi: 10.1371/journal.pcbi.1012864 Q13.62024

Logic-based modeling of biological networks with Netflux

基于逻辑的生物网络建模方法Netflux 翻译改进

Alexander P Clark  1, Mukti Chowkwale  1, Alexander Paap  1, Stephen Dang  1, Jeffrey J Saucerman  1  2

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作者单位

  • 1 Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
  • 2 Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America.
  • DOI: 10.1371/journal.pcbi.1012864 PMID: 40184419

    摘要 中英对照阅读

    Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux. Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

    Keywords:logic based modeling; biological networks

    分子信号网络驱动细胞做出各种决策,包括是否增殖、何时以及如何死亡,以及其他许多过程。这些网络通常连接数百种蛋白质、基因和过程。理解这些复杂的网络可以通过计算建模来帮助实现,但这些工具需要大量的编程知识。在本文中,我们介绍了一个用户友好的无需编程的网络模拟工具Netflux。在过去十年里,Netflux已被用来构建多个预测性网络模型,加深了我们对复杂生物网络如何做出细胞决策的理解。在这里,我们将提供一个Netflux教程,涵盖如何构建网络模型以及如何模拟对扰动的网络响应。完成本教程后,您将能够在Netflux中创建自己的模型,并模拟蛋白质和基因变化在网络信号传导和基因调控网络中的传播。

    关键词:逻辑基于建模; 生物网络

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    期刊名:Plos computational biology

    缩写:PLOS COMPUT BIOL

    ISSN:1553-734X

    e-ISSN:1553-7358

    IF/分区:3.6/Q1

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