{"title":"A review of advances in integrating gene regulatory networks and metabolic networks for designing strain optimization","authors":"Ridho Ananda, Kauthar Mohd Daud, Suhaila Zainudin","doi":"10.1016/j.jksuci.2024.102120","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400209X/pdfft?md5=586fad615ff045ca1e3c6db05e62f27d&pid=1-s2.0-S131915782400209X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S131915782400209X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.