Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli.
BACKGROUND
Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner.


RESULTS
Most existing studies for inferring transcriptional regulatory interactions and the activity of TFs ignore the possible post-transcriptional effects of ncRNAs. In this work, we propose a novel framework to infer the activity of regulators including both TFs and ncRNAs by exploring the expression profiles of target genes and (post)transcriptional regulatory relationships. We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem. The inference process is achieved by an iterative algorithm, in which two linear programming models are efficiently solved. In contrast to available related studies, the effects of ncRNAs on transcription process are considered in this work, and thus more reasonable and accurate reconstruction can be expected. In addition, the approach is suitable for large-scale problems from the viewpoint of computation. Experiments on two synthesized data sets and a model system of Escherichia coli (E. coli) carbon source transition from glucose to acetate illustrate the effectiveness of our model and algorithm.


CONCLUSIONS
Our results show that incorporating the post-transcriptional regulation of ncRNAs into system model can mine the hidden effects from the regulation activity of TFs in transcription processes and thus can uncover the biological mechanisms in gene regulation in a more accurate manner. The software for the algorithm in this paper is available upon request.
School of Information, Renmin University of China, Beijing 100872, PR China. wangrsh@amss.ac.cn
Rui-Sheng
RS
Wang
Guangxu
G
Jin
Xiang-Sun
XS
Zhang
Luonan
L
Chen
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D015964:Gene Expression Regulation, Bacterial; D008957:Models, Genetic; D012323:RNA Processing, Post-Transcriptional; D022661:RNA, Untranslated / Q000378:metabolism*
D022661:RNA, Untranslated