BACKGROUND: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. RESULTS: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. CONCLUSIONS: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

• Keywords
• Cytoscape, Gene network inference, Time series, Transcription regulation,
• MeSH
• Bacteria genetics   MeSH
• Time Factors MeSH
• Databases, Genetic * MeSH
• Eukaryota genetics   MeSH
• Gene Regulatory Networks * MeSH
• Humans MeSH
• Gene Expression Regulation * MeSH
• Regulon genetics   MeSH
• Reproducibility of Results MeSH
• Saccharomyces cerevisiae genetics   MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network--the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.

• MeSH
• Time Factors MeSH
• Cyclins genetics   metabolism   MeSH
• Gene Deletion MeSH
• Gene Regulatory Networks genetics   MeSH
• Models, Genetic MeSH
• Mutagenesis genetics   MeSH
• Gene Expression Regulation, Fungal MeSH
• Saccharomyces cerevisiae Proteins genetics   metabolism   MeSH
• Saccharomyces cerevisiae genetics   MeSH
• Gene Expression Profiling MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Cyclins MeSH
• Saccharomyces cerevisiae Proteins MeSH

This review summarizes the main results obtained in the fields of general and molecular microbiology and microbial genetics at the Institute of Microbiology of the Academy of Sciences of the Czech Republic (AS CR) [formerly Czechoslovak Academy of Sciences (CAS)] over more than 50 years. Contribution of the founder of the Institute, academician Ivan Málek, to the introduction of these topics into the scientific program of the Institute of Microbiology and to further development of these studies is also included.

• MeSH
• Academies and Institutes history   MeSH
• History, 20th Century MeSH
• Genetics, Microbial history   MeSH
• Molecular Biology history   MeSH
• Check Tag
• History, 20th Century MeSH
• Publication type
• Journal Article MeSH
• Historical Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Geographicals
• Czech Republic MeSH

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