人工蜂群算法用于估计酵母发酵途径的动力参数
Ahmad Muhaimin Ismail1, Muhammad Akmal Remli2,3, Yee Wen Choon2,3
1Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
Journal of integrative bioinformatics
|June 21, 2023
概括
准确的动力参数对于系统生物学模型至关重要. 人工蜂群 (ABC) 算法有效地估计了Saccharomyces cerevisiae发酵途径的这些参数,提高了模拟的准确性.
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:
- 准确的运动参数对于模拟系统生物学中的体内生物过程至关重要.
- 对于复杂的生物模型的参数估计,如Saccharomyces cerevisiae发酵途径,由于模型的非线性和无法直接测量运动参数而具有挑战性.
研究的目的:
- 提出和评估人工蜂群 (ABC) 算法,用于估计Saccharomyces cerevisiae发酵途径中的动力参数.
- 为了获得更准确的动力参数值,以改进体内过程的模拟.
主要方法:
- 使用了人工蜂群 (ABC) 算法,这是一种以自然为灵感的优化技术.
- 应用了ABC算法来估计Saccharomyces cerevisiae发酵途径中一个关键代谢物的六个参数.
- 将ABC算法的性能与其他估计算法的性能进行比较.
主要成果:
- 与其他估计算法相比,ABC算法表现出卓越的性能.
- 由ABC估计的动力参数值对于模拟模型来说更准确.
- 大多数估计的动力参数与实验数据密切匹配.
结论:
- 人工蜂群 (ABC) 算法是复杂生物模型中参数估计的有效工具.
- 使用ABC精确的动力参数估计提高了Saccharomyces cerevisiae发酵途径系统生物学模拟的可靠性.
- 这种方法可以导致更精确地理解和优化代谢过程.
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