印度储备银行:用于设计最佳突变菌株的监管代谢网络模型的新算法
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
概括
研究人员开发了一种新的基于可靠性的整合 (RBI) 算法,通过结合基因调节网络 (GRN) 布尔规则来改进代谢工程. 这提高了最佳突变微生物菌株的设计,以提高代谢物产量.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 计算生物学 计算生物学
背景情况:
- 整合基因调节网络 (GRNs) 和代谢网络对于in silico代谢工程至关重要.
- 现有的模型往往无法将GRNs和基因-蛋白质反应 (GPR) 相互作用的布尔规则纳入,忽视了激活和抑制等基因相互作用类型.
- 这种限制可能导致代谢物生产增强的非最佳结果.
研究的目的:
- 通过使用可靠性理论呈现一种新型模型,该模型整合了经验GRNs和GPR相互作用的布尔规则.
- 引入基于可靠性的整合 (RBI) 算法及其变体 (RBI-T1,RBI-T2,RBI-T3).
- 与现有方法相比,评估RBI算法的性能和效率.
主要方法:
- 开发基于可靠性的整合 (RBI) 算法,结合布尔规则.
- 使用实证数据和转录因子 (TF) 淘汰方案验证RBI算法.
- 对RBI算法的比较分析与性能和计算复杂性的现有方法.
- 实施RBI方法设计最佳突变菌株的*大肠杆菌*和*麦芽菌.
主要成果:
- 印度储备银行算法表现出强大的有效性和效率,与现有算法具有竞争力.
- 印度储备银行 (RBI) 的方法成功确定了八个方案,以提高糖酸盐和乙醇生产率,同时保持微生物菌株的生存能力.
- 模拟结果证实了RBI方法在整合GRN和代谢网络信息方面的优势.
结论:
- 拟议的RBI算法对于in silico代谢工程是有效和高效的.
- 印度央行算法准确地结合了布尔规则和GPR相互作用,克服了以前模型的局限性.
- 建议使用RBI方法来构建最佳突变菌株,以改善微生物系统中的代谢物产量.
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