Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.
Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.
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DOI：
10.1093/bioinformatics/btt393
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发表时间：
2013-09-15
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影响因子：
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通讯作者：
Saez-Rodriguez J
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文献类型：
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作者：
Guziolowski C;Videla S;Eduati F;Thiele S;Cokelaer T;Siegel A;Saez-Rodriguez J
动机：逻辑建模是研究跨多个通路的信号转导的有用工具。逻辑模型可以通过训练包含磷酸化蛋白质组学数据的先验知识的网络来生成。训练可以使用随机优化过程来执行，但是这些不能保证全局最优或报告完整的可行模型族。然而，这对于提供对信号转导机制的精确洞察并产生可靠的预测至关重要。结果：我们提出了使用答案集编程来详尽地探索可行的逻辑模型的空间。为此，我们开发了一个开源Python包，它提供了一个强大的平台，通过利用丰富的建模语言和答案集编程的求解技术来学习和描述逻辑模型。我们通过回顾肝细胞中促生长和炎症通路的模型来说明Caspo的有用性。我们表明，如果考虑到实验误差，有成千上万（11 700）的模型与数据兼容。尽管数量很大，我们可以从模型中提取结构特征，例如总是（或从不）存在的链接或以相互排斥的方式出现的模块。为了进一步描述这类模型，我们研究了模型的输入-输出行为。我们在11700个模型中发现了91种行为，并提出了新的实验来区分它们。我们的研究结果强调了以全局和详尽的方式表征可行模型家族的重要性，对实验设计具有重要意义。可用性：Caspo可免费下载（许可证GPLv 3），并作为Web服务在http://caspo.genouest.org/上提供。补充信息：补充材料可在Bioinformatics在线获得。联系人：圣地亚哥. irisa.fr
Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input–output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary information: Supplementary materials are available at Bioinformatics online. Contact: santiago.videla@irisa.fr