TFBMiner

TFBMiner mines transcription factor-based biosensors responsive to small molecules by identifying gene clusters involved in metabolite catabolism and their transcriptional regulators.

Key Features:

• Heuristic rule-based identification: Implements a heuristic rule-based model to identify gene clusters associated with the catabolism of user-specified molecules and their cognate transcriptional regulators.
• Gene organization analysis: Analyzes gene organization patterns to associate metabolic pathways with nearby regulatory genes.
• Candidate scoring and ranking: Scores and ranks potential transcription factor candidates based on their fit to the heuristic model.
• Validation across compound classes: Validated on sugars, amino acids, and aromatic compounds and used to identify a novel biosensor responsive to S-mandelic acid.
• Enables biosensor discovery for synthetic biology: Identifies transcription factors that can serve as genetically encoded biosensors for constructing self-regulating biosynthetic pathways and probing microbial gene regulatory networks.

Scientific Applications:

• Environmental monitoring: Discovery of transcription factor-based biosensors for detection of environmental contaminants.
• Medical diagnostics: Identification of transcription factors responsive to biomarkers for potential diagnostic biosensors.
• Industrial biotechnology: Selection of biosensors for microbial strain optimization and metabolic engineering.
• Synthetic biology research: Expansion of detectable metabolite repertoires to enable construction of self-regulating pathways and study of metabolite-responsive regulatory networks.

Methodology:

Uses a heuristic rule-based model to analyze gene organization patterns associated with metabolite catabolism and regulatory mechanisms, predicts potential transcription factor candidates, and scores candidates based on alignment with the model.