Towards Using Probabilities and Logic to Model Regulatory Networks

António Gonçalves, I. Ong, Jeffrey A. Lewis, V. S. Costa
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引用次数: 1

Abstract

Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.
利用概率和逻辑建模监管网络
转录调控在细胞的每一个决定中都起着重要的作用。不幸的是,仅凭直觉很难理解控制细胞如何对各种环境线索作出反应的动力学。我们介绍了基于统计关系学习最新研究成果的基于逻辑的调控模型,并通过分析Hog1通路的时间序列基因表达数据来验证我们的方法。我们的研究结果表明,合理的调控网络可以使用概率逻辑模型从时间序列基因表达数据中学习。因此,网络假设可以从现有的基因表达数据中生成,供实验生物学家使用。
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