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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
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Related Experiment Video

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates.

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    This study introduces a Bayesian method to infer gene regulatory networks from gene expression data with replicates. The approach identifies robust network features, validated by simulated and Arabidopsis thaliana data.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Gene expression time course data with replicates is crucial for understanding transcriptional networks.
    • Inferring gene regulatory networks from such data presents significant challenges.
    • Hierarchical structures often exist within replicate datasets.

    Purpose of the Study:

    • To develop a Bayesian inference method for identifying gene transcriptional networks.
    • To incorporate hierarchical structures present in multiple replicate gene expression data.
    • To calculate posterior probabilities for network features.

    Main Methods:

    • Application of a hierarchical multiple replicate algorithm.
    • Utilizing Bayesian inference, Metropolis-Hastings algorithm, and model averaging.
    • Testing the algorithm on simulated datasets and Arabidopsis thaliana gene expression data.

    Main Results:

    • The method assigns high posterior probabilities to gene pairs with moderate signal partial correlation in simulated data.
    • Analysis of Arabidopsis thaliana data revealed high posterior probability edges consistent with existing literature.
    • Demonstrated the effectiveness of the Bayesian approach for network inference.

    Conclusions:

    • The developed Bayesian algorithm effectively infers gene regulatory network features from hierarchical, replicated gene expression data.
    • The approach provides a robust framework for hypothesis generation in systems biology.
    • Findings support the utility of the method for biological network discovery.