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

Ridho Ananda1,2, Kauthar Mohd Daud1, Suhaila Zainudin1

  • 1Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Kajang, Selangor, Malaysia.

Peerj. Computer Science
|June 27, 2025
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Summary
This summary is machine-generated.

Researchers developed a new reliability-based integrating (RBI) algorithm to improve in silico metabolic engineering by incorporating gene regulatory network (GRN) Boolean rules. This enhances the design of optimal mutant microbial strains for higher metabolite production.

Keywords:
Flux balance analysisGene regulatory networksIn silico metabolic engineeringMetabolic networksMutant strainReliability theory

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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.