Post-transcriptional control of mRNA by micro-RNAs (miRNAs) represents an important mechanism of gene regulation. miRNAs act by binding to the 3' untranslated region (3'UTR) of an mRNA, affecting the stability and translation of the target mRNA. Here, we present a numerical model of miRNA-mediated mRNA downregulation and its application to analysis of temporal microarray data of HepG2 cells transfected with miRNA-124a. Using the model our analysis revealed a novel mechanism of mRNA accumulation control by miRNA, predicting that specific mRNAs are controlled in a digital, switch-like manner. Specifically, the contribution of miRNAs to mRNA degradation is switched from maximum to zero in a very short period of time. Such behaviour suggests a model of control in which mRNA is at a certain moment protected from binding of miRNA and further accumulates with a basal rate. Genes associated with this process were identified and parameters of the model for all miRNA-124a affected mRNAs were computed.

• MeSH
• Cell Line MeSH
• Down-Regulation MeSH
• Humans MeSH
• RNA, Messenger metabolism   MeSH
• MicroRNAs metabolism   MeSH
• Models, Genetic * MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• RNA Stability MeSH
• Gene Silencing * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• RNA, Messenger MeSH
• MicroRNAs MeSH

Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found.

• MeSH
• Algorithms * MeSH
• Genes, cdc MeSH
• Transcription, Genetic MeSH
• Gene Regulatory Networks MeSH
• Linear Models MeSH
• Least-Squares Analysis MeSH
• Models, Genetic * MeSH
• Gene Expression Regulation, Fungal * MeSH
• Saccharomyces cerevisiae genetics   MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• Gene Expression Profiling MeSH
• Transcription Factors analysis   MeSH
• Computational Biology MeSH
• Publication type
• Journal Article MeSH
• Evaluation Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH
• Names of Substances
• Transcription Factors MeSH