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Updated: Jun 4, 2026
12:54
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
Published on: March 7, 2018
Large-scale learning of combinatorial transcriptional dynamics from gene expression
H M Shahzad Asif1, Guido Sanguinetti
1School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, UK.
Bioinformatics (Oxford, England)
|March 4, 2011
Summary
This study introduces a new statistical method to understand how multiple transcription factors (TFs) work together to regulate genes. The approach infers TF activities from gene expression data, revealing complex combinatorial gene regulation patterns.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Understanding transcription factor (TF) activation is crucial for gene regulation dynamics.
- Experimental measurement of TF activity is challenging, necessitating computational inference from mRNA expression.
- Existing models often overlook the combinatorial nature of transcriptional regulation essential for signal integration.
Purpose of the Study:
- To develop a novel statistical method for inferring combinatorial gene regulation by multiple TFs.
- To address limitations in current models by incorporating the combinatorial aspect of transcriptional regulation.
Main Methods:
- Implementation of a factorial hidden Markov model (HMM).
- Utilizing a non-linear likelihood function to model interactions between hidden TFs.
- Application to large-scale transcriptional regulatory networks and genome-wide expression datasets.
Main Results:
- Successful inference of combinatorial regulation by multiple TFs.
- Demonstrated applicability on artificial and three real genome-wide expression datasets.
- Biologically coherent results supporting the exploration of combinatorial transcriptional regulation.
Conclusions:
- The developed method effectively infers combinatorial TF regulation.
- Provides a valuable tool for investigating complex gene regulatory mechanisms.
- Advances the understanding of how multiple TFs integrate signals to control gene expression.
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