A review of advances in integrating gene regulatory networks and metabolic networks for designing strain optimization

Abstract

Strain optimization aims to overproduce valuable metabolites by leveraging an understanding of biological systems, including metabolic networks and gene regulatory networks (GRNs). Accordingly, researchers proposed integrating metabolic networks and GRNs to be analyzed simultaneously. The proposed algorithms from 2002 to 2021 were rFBA, SR-FBA, iFBA, PROM, PROM2.0, TIGER, BeReTa, CoRegFlux, IDREAM, TRFBA, OptRAM, TRIMER, and PRIME. Each algorithm has different characteristics. Thus, using the appropriate algorithm for designing strain optimization is essential. Therefore, a critical review was conducted by synthesizing and analyzing the existing algorithms. Five aspects are discussed in this review: the strategic approaches, model of GRNs, source of GRNs, optimization, supplementary methods, and the programming language used. Based on the review, several algorithms were better at modeling integrated regulatory-metabolic networks with high confidence, i.e., PROM, PROM2.0, and TRFBA. A simulation was applied to six strains. The results show that PROM2.0 best predicted the production rate and time complexity. However, the model is heavily influenced by the quality and quantity of the gene expression data. In addition, there are inconsistencies between GRNs and the gene expression data. Thus, this review also discussed future work based on GRNs and gene expression data.

Keywords

• In silico metabolic engineering
• Metabolic networks
• Gene regulatory networks
• Constraint-based modeling
• Strain optimization