RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Integrating gene regulatory networks (GRNs) and metabolic networks is crucial for in silico metabolic engineering.
• Existing models often fail to incorporate Boolean rules from GRNs and gene-protein-reaction (GPR) interactions, neglecting gene interaction types like activation and inhibition.
• This limitation can lead to suboptimal outcomes in metabolite production enhancement.

Purpose of the Study:

• To present a novel model that integrates Boolean rules of empirical GRNs and GPR interactions using reliability theory.
• To introduce the reliability-based integrating (RBI) algorithm and its variants (RBI-T1, RBI-T2, RBI-T3).
• To assess the performance and efficiency of the RBI algorithms compared to existing methods.

Main Methods:

• Development of the reliability-based integrating (RBI) algorithm incorporating Boolean rules.
• Validation of RBI algorithms using empirical data and transcription factor (TF) knockout schemes.
• Comparative analysis of RBI algorithms against existing methods for performance and computational complexity.
• Implementation of the RBI method for designing optimal mutant strains of *Escherichia coli* and *Saccharomyces cerevisiae*.

Main Results:

• RBI algorithms demonstrated strong effectiveness and efficiency, proving competitive with existing algorithms.
• The RBI method successfully identified eight schemes to enhance succinate and ethanol production rates while maintaining microbial strain viability.
• Simulation results confirmed the superiority of the RBI approach in integrating GRN and metabolic network information.

Conclusions:

• The proposed RBI algorithms are effective and efficient for in silico metabolic engineering.
• RBI algorithms accurately incorporate Boolean rules and GPR interactions, overcoming limitations of previous models.
• The RBI approach is recommended for constructing optimal mutant strains to improve metabolite production in microbial systems.