Probing transcription factor subsets in gene regulatory networks

Area of Science:

• Bioinformatics
• Systems Biology
• Network Science

Background:

• Transcription factors (TFs) are critical regulators of gene expression, implicated in various diseases.
• The regulatory relationships between TFs and genes form complex networks.
• Identifying influential TFs is vital for perturbation studies and therapeutic targeting.

Purpose of the Study:

• To develop and evaluate methods for identifying sets of transcription factors that exert maximum regulatory influence on target genes.
• To model the TF-gene regulatory network as a probing problem within a bipartite graph framework.
• To assess the performance of different algorithms on simulated and real-world biological data.

Main Methods:

• Modeling the TF-gene regulatory network as a bipartite graph.
• Applying adaptive and non-adaptive probing algorithms to identify influential TF sets.
• Testing algorithms on simulated network data to analyze properties and adaptivity gap.
• Validating the approach on real-life datasets related to T-cell immunity and lymphoid leukemia.

Main Results:

• Demonstrated the efficacy of network-based probing algorithms in identifying key transcription factors.
• Quantified the adaptivity gap between different algorithmic strategies on simulated data.
• Successfully identified relevant TFs regulating genes in T-cell mediated immunity and lymphoid leukemia using real data.
• The proposed method shows minimal data requirements.

Conclusions:

• The bipartite graph probing approach offers an effective strategy for identifying maximally influential transcription factors.
• This method is adaptable and has broad applicability to diverse biological networks and perturbation studies.
• The findings contribute to a deeper understanding of gene regulatory networks and disease mechanisms.