Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli

Rui-Sheng Wang,Luonan Chen,Xiang-Sun Zhang,Guangxu Jin

doi:10.1186/1471-2105-10-s4-s6

Abstract

BackgroundTranscriptional 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.ResultsMost 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.ConclusionOur 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.

Highlights

Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles
In light of existing work for studying transcriptional regulation and regulator activities that ignores the possible post-transcriptional effects of small non-coding RNA (sRNA) on mRNA level, in this paper, we propose a novel approach to infer the activity of regulators including TFs and sRNAs
We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem

Summary

Introduction

Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. Transcription regulation of gene expression is one of the most important processes in molecular biology It transmits static information encoded in the DNA sequence into functional protein molecules which in turn control most of the cellular processes. It is some DNA-binding proteins known as transcription factors (TFs) that achieve the transcriptional regulation of genes. There have been great efforts contributed to identify transcription factors and generate binding data for many organisms [1,2] Another important problem is to synthesize and analyze transcriptional regulatory networks from ChIP-chip data and gene expression profiles [3,4,5]. More detailed surveys about these topics can be found in [6,7]

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