A hybrid of Bees algorithm and regulatory on/off minimization for optimizing lactate and succinate production.

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Journal of Integrative Bioinformatics Pub Date : 2022-07-19 eCollection Date: 2022-09-01 DOI:10.1515/jib-2022-0003

Mohd Izzat Yong, Mohd Saberi Mohamad, Yee Wen Choon, Weng Howe Chan, Hasyiya Karimah Adli, Khairul Nizar Syazwan Wsw, Nooraini Yusoff, Muhammad Akmal Remli

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Abstract

Metabolic engineering has expanded in importance and employment in recent years and is now extensively applied particularly in the production of biomass from microbes. Metabolic network models have been employed extravagantly in computational processes developed to enhance metabolic production and suggest changes in organisms. The crucial issue has been the unrealistic flux distribution presented in prior work on rational modelling framework adopting Optknock and OptGene. In order to address the problem, a hybrid of Bees Algorithm and Regulatory On/Off Minimization (BAROOM) is used. By employing Escherichia coli as the model organism, the most excellent set of genes in E. coli that can be removed and advance the production of succinate can be decided. Evidences shows that BAROOM outperforms alternative strategies used to escalate in succinate production in model organisms like E. coli by selecting the best set of genes to be removed.

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混合蜜蜂算法和调节开/关最小化优化乳酸和琥珀酸盐生产。

近年来，代谢工程的重要性和应用范围不断扩大，目前已广泛应用于微生物生物质的生产。代谢网络模型已被大量应用于计算过程中，这些计算过程旨在提高代谢产生并提示生物体的变化。关键问题是在之前的工作中，采用Optknock和OptGene的合理建模框架提出了不切实际的通量分布。为了解决这个问题，使用了蜜蜂算法和调节开/关最小化(BAROOM)的混合算法。利用大肠杆菌作为模式生物，可以确定大肠杆菌中最优秀的一组基因，可以去除并促进琥珀酸盐的生产。有证据表明，BAROOM通过选择要去除的最佳基因集，优于用于在大肠杆菌等模式生物中增加琥珀酸盐产量的替代策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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