kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.
kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.
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DOI：
10.1186/s12859-023-05329-6
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发表时间：
2023-06-12
影响因子：
3
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中科院分区：
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细胞信号网络的计算模型是探索底层系统行为和预测对各种扰动的响应的极其有用的工具。通过将信号级联表示为可执行的布尔网络，先前开发的 rxncon（“反应应急”）形式和相关的 Python 包即使在大型（数千个组件）生物系统中也能实现准确且可扩展的信号转导建模。这些模型分为反应（生成状态）和意外事件（影响反应）；这避免了所谓的系统规模的“组合爆炸”。生物系统的布尔描述弥补了定量模型所需的动力学参数的可用性较差的问题。不幸的是，很少有工具可用于支持 rxncon 模型开发，特别是对于大型、复杂的系统。 我们提供了 kboolnet 工具包（https://github.com/Kufalab-UCSD/kboolnet，完整文档位于 https://github.com/Kufalab-UCSD/kboolnet/wiki）、一个 R 包和一组脚本，它们与基于 python 的 rxncon 软件无缝集成，共同提供完整的验证工作流程， rxncon 模型的验证和可视化。验证脚本VerifyModel.R 检查对重复刺激的响应以及稳态行为的一致性。验证脚本 TruthTable.R、SensitivityAnalysis.R 和 ScoreNet.R 提供了各种读数，用于模型预测与实验数据的比较。特别是，ScoreNet.R 将模型预测与云存储的 MIDAS 格式实验数据库进行比较，以提供用于跟踪模型准确性的数值分数。最后，可视化脚本允许以图形方式表示模型拓扑和行为。整个kboolnet工具包支持云，可以轻松协作开发；大多数脚本还允许提取和分析单个用户定义的“模块”。 kboolnet 工具包提供了一个模块化、支持云的工作流程，用于 rxncon 模型的开发及其验证、验证和可视化。这将使未来能够使用 rxncon 形式创建更大、更全面、更严格的细胞信号传导模型。在线版本包含可在 10.1186/s12859-023-05329-6 获取的补充材料。
Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon (“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems. We present the kboolnet toolkit (https://github.com/Kufalab-UCSD/kboolnet, complete documentation at https://github.com/Kufalab-UCSD/kboolnet/wiki), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”. The kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future. The online version contains supplementary material available at 10.1186/s12859-023-05329-6.
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DOI：
10.1186/1478-811x-11-43
发表时间：
2013-06-26
期刊：
Cell communication and signaling : CCS
影响因子：
--
作者：
Samaga R;Klamt S
通讯作者：
Klamt S
DOI：
10.1093/bioinformatics/btt393
发表时间：
2013-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Guziolowski C;Videla S;Eduati F;Thiele S;Cokelaer T;Siegel A;Saez-Rodriguez J
通讯作者：
Saez-Rodriguez J
影响因子：
3.7
作者：
Hung-Cuong Trinh，Hung-Cuong;Kwon，Yung-Keun
通讯作者：
Kwon，Yung-Keun
影响因子：
--
作者：
Flöttmann M;Krause F;Klipp E;Krantz M
通讯作者：
Krantz M
影响因子：
5.8
作者：
Saez-Rodriguez, Julio;Goldsipe, Arthur;Sorger, Peter K.
通讯作者：
Sorger, Peter K.