Emergent Principles in Gene Expression Dynamics

Q3 Computer Science Open Bioinformatics Journal Pub Date : 2011-02-02 DOI:10.2174/1875036201105010034
J. Nacher, T. Ochiai
{"title":"Emergent Principles in Gene Expression Dynamics","authors":"J. Nacher, T. Ochiai","doi":"10.2174/1875036201105010034","DOIUrl":null,"url":null,"abstract":"Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emer- gence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to recon- struct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of en- zyme-catalyzed reaction routes and Markov processes as well as combination of complex network properties with sto- chastic theory.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"5 1","pages":"34-41"},"PeriodicalIF":0.0000,"publicationDate":"2011-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Bioinformatics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1875036201105010034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1

Abstract

Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emer- gence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to recon- struct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of en- zyme-catalyzed reaction routes and Markov processes as well as combination of complex network properties with sto- chastic theory.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基因表达动力学中的涌现原理
全基因组基因表达数据处理的快速发展使我们能够初步了解细胞内组织的一些基本规律和原理,并研究其复杂的调控结构。然而,识别网络中涉及的动态过程的共性并没有遵循相同的发展。特别是,动力学和结构特征之间的耦合在很大程度上仍未发现。在这里,我们回顾了几项研究,这些研究利用微阵列时间序列数据揭示了不同环境条件和疾病状态下基因表达的动态和原理,以及利用信息论揭示了基因表达系统中临界性的出现。此外,我们还描述了通过使用转录动力学信息来探索表征基因网络的问题所做的努力。将转录组织中揭示的涌现原理与动态信息相结合,可能导致基因网络的重构、表征和完整。我们还讨论了几种基于一系列酶催化反应路线和马尔可夫过程的模拟方法,以及将复杂网络性质与随机理论相结合的方法。
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 去求助
来源期刊
Open Bioinformatics Journal
Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
期刊介绍: The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.
期刊最新文献
Decision-making Support System for Predicting and Eliminating Malnutrition and Anemia Immunoinformatics Approach for the Design of Chimeric Vaccine Against Whitmore Disease A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease Early Prediction of Covid-19 Samples from Chest X-ray Images using Deep Learning Approach Electronic Health Record (EHR) System Development for Study on EHR Data-based Early Prediction of Diabetes Using Machine Learning Algorithms
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1