Computing interaction probabilities in signaling networks.

EURASIP journal on bioinformatics & systems biology Pub Date : 2015-11-11 eCollection Date: 2015-12-01 DOI:10.1186/s13637-015-0031-8
Haitham Gabr, Juan Carlos Rivera-Mulia, David M Gilbert, Tamer Kahveci
{"title":"Computing interaction probabilities in signaling networks.","authors":"Haitham Gabr, Juan Carlos Rivera-Mulia, David M Gilbert, Tamer Kahveci","doi":"10.1186/s13637-015-0031-8","DOIUrl":null,"url":null,"abstract":"<p><p>Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions between molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also alter biological processes like transcription or translation. This uncertainty is often modeled by associating each interaction with a probability value. Studying biological networks under this probabilistic model has already been shown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate probability values to interactions remains unresolved. In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. The transcription levels define the signal reachability probability between membrane receptors and transcription factors. Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and Genomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven major leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes affects signaling interactions.</p>","PeriodicalId":101453,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2015 1","pages":"10"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13637-015-0031-8","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP journal on bioinformatics & systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13637-015-0031-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2015/12/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions between molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also alter biological processes like transcription or translation. This uncertainty is often modeled by associating each interaction with a probability value. Studying biological networks under this probabilistic model has already been shown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate probability values to interactions remains unresolved. In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. The transcription levels define the signal reachability probability between membrane receptors and transcription factors. Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and Genomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven major leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes affects signaling interactions.

信令网络中交互概率的计算。
生物网络本质上具有不确定的拓扑结构。这是由许多因素引起的。例如,分子之间的相互作用在不同的条件下可能发生,也可能不发生。遗传或表观遗传突变也可能改变转录或翻译等生物过程。这种不确定性通常通过将每个交互作用与概率值相关联来建模。在这种概率模型下研究生物网络已经被证明可以对相互作用数据进行准确而深刻的分析。然而,为相互作用分配准确的概率值的问题仍然没有解决。在本文中,我们提出了一种基于基因转录水平计算信号网络中相互作用概率的新方法。转录水平决定了膜受体与转录因子之间信号可达的概率。我们的方法计算交互概率,使观测到的和计算得到的信号可达概率之间的差距最小化。我们在京都基因与基因组百科全书(KEGG)中的四个信号网络上评估了我们的方法。对于每个网络,我们使用七种主要白血病亚型的基因表达谱计算其边缘概率。我们使用这些值来分析不同白血病亚型诱导的应激如何影响信号相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信