NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.
Kenong Su1, Ataur Katebi2,3, Vivek Kohar4
1Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA.
Genome Biology
|December 27, 2022
Summary
We developed NetAct, a computational platform to identify core gene regulatory networks. NetAct accurately infers transcription factor activity and network construction for biological decision-making.
Area of Science:
- Systems biology
- Computational biology
- Gene regulatory networks
Background:
- Identifying core gene regulatory circuits is crucial for understanding biological decision-making.
- Existing methods for network construction have limitations in accuracy and scope.
Purpose of the Study:
- To develop a computational platform, NetAct, for constructing core transcription factor regulatory networks.
- To robustly infer transcription factor activity and validate network models.
Main Methods:
- NetAct integrates transcriptomics data with literature-based transcription factor-target databases.
- It infers regulator activity from target gene expression.
- Network construction is based on transcriptional activity, with mathematical modeling for validation.
Main Results:
- NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks in silico benchmark tests.
- The platform successfully models networks driving key biological processes like epithelial-mesenchymal transition and macrophage polarization.
Conclusions:
- NetAct provides a robust and accurate computational approach for constructing core gene regulatory networks.
- This platform can advance the understanding of biological decision-making processes and disease mechanisms.
Keywords:
Cellular state transitionsEpithelial-mesenchymal transitionGene regulatory circuitsGene regulatory networksMacrophage polarizationMathematical modelingSystems biologyTranscriptional activityMore Related Videos
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