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FastKnock:一种高效的下一代方法,用于识别所有淘汰策略,以优化菌株

Leila Hassani¹, Mohammad R Moosavi¹, Payam Setoodeh¹

¹Shiraz University.

Research square

|July 28, 2023

概括

FastKnock是一个新的计算算法,用于识别代谢工程的基因淘汰策略. 它显著加快了为生物化学品寻找最佳生产菌株的过程.

科学领域:

代谢工程是代谢工程.
合成生物学合成生物学
计算生物学计算生物学

背景情况:

代谢工程旨在优化微生物中的生化生产.
确定最佳的基因淘汰策略对于菌株设计至关重要.
现有的计算算法在效率和解决方案完整性方面面临挑战.

研究的目的:

开发一种高效的算法,用于识别所有可能的基因淘汰策略,用于增长合过度生产.
显著减少代谢工程目标识别的计算搜索空间和执行时间.
为选择最佳干预策略提供全面的解决方案.

主要方法:

开发了FastKnock,这是一个下一代算法,利用专门的深度首次穿越.
实施了搜索空间的显著修剪,以确定淘汰策略.
在不同的介质条件下,对三种*Escherichia coli*基因组规模的代谢模型进行了评估FastKnock性能.

主要成果:

FastKnock 极大地缩小了搜索空间,在四重冲击中达到 <0.2%,在五重冲击中达到 <0.02%.
与OptKnock和MCSEnumerator相比,该算法识别了更多的实际解决方案.
在不同的大肠杆菌模型和介质条件中证明了高效的性能.

结论:

FastKnock提供了一种高效和全面的方法来识别代谢工程目标.
该算法可以发现许多可行的策略,用于增强生化生产.
Python 实现的公开可用性促进了在菌株开发中的更广泛应用.

关键词:

生化过度生产生化过度生产基因组规模的代谢模型.与生长相结合的生物合成.数学优化的数学优化反应聚类反应聚类.反应淘汰赛策略反应淘汰赛策略搜索空间减少,减少了搜索空间.

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