ReProMin

ReProMin identifies minimal transcriptional regulatory interventions to reallocate proteome resources in Escherichia coli and increase capacity for recombinant expression.

Key Features:

• Rational Modification of Transcriptional Networks: Identifies a minimal set of transcription factor (TF) perturbations, categorizes TFs by the essentiality of the genes they regulate, and ranks them using proteomic data to compute a proteomic balance defined as the net proteomic charge released.
• Combinatorial Approach: Employs a combinatorial strategy to design combinations of TF removals that maximize release of proteomic charge while targeting specificity to minimize unintended effects.
• Experimental Validation: Predictions are validated through experimental expression profiling, and engineered strains demonstrate increased production yields for molecules derived from recombinant metabolic pathways.
• Minimal Genetic Interventions: Achieves substantial proteome reduction with few mutations, exemplified by a case where three mutations were predicted to release 0.5% of the proteome and increase the proteome budget for recombinant expression.

Scientific Applications:

• Strain engineering for proteome reallocation: Guides design of Escherichia coli strains with reduced non-essential proteomic load to free resources for heterologous expression.
• Reduction of cellular burden: Enables targeted deletions of TF-activated proteomes to lower cellular burden and improve production of proteins and metabolites such as violacein.
• Integration of regulatory and proteomic data: Combines regulatory network information and whole-cell proteomic data to identify transcription factor–activated proteomes for precise interventions.
• Design of desired cellular phenotypes: Uses regulatory manipulation as a control layer to predictably optimize strains for specific synthetic biology tasks.

Methodology:

Computational steps include identifying a minimal set of TF interventions, categorizing TFs by essentiality of their regulated genes, ranking TFs by proteomic balance (net proteomic charge released) using proteomic data, and applying a combinatorial strategy to select TF removal combinations that maximize released proteomic charge.