在批量和半批量反应器中对阿莫西西林合成的数学建模:贝叶斯统计学和遗传算法的应用
Lucas Figueiredo Formigosa1, Ingrid Cabral Dos Santos1, Letícia Eduarda Alves E Álvares2
1Faculty of Biotechnology, Federal University of Pará, Belém, Pará, Brazil.
这项研究优化了使用固定性青素G乙酶 (PGA) 优化阿莫西林的生产. 马尔科夫链蒙特卡洛 (MCMC) 方法准确预测了酶合成动力学,改善了抗生素产量.
科学领域:
- 生物催化和酶合成的生物催化.
- 化学动力学和反应堆工程学
- 制药制造业 制药制造业 制药制造业
背景情况:
- 氨基氨酸的合成依赖于酶的过程,通常涉及青素G乙酶 (PGA).
- 优化酶反应需要了解动力学特性,并尽量减少通过水解产品的损失.
- 固定酶在稳定性和可重复使用性方面具有优势,可用于工业应用.
研究的目的:
- 为了研究动不动的青素G乙酶 (PGA) 的动力特性,用于氨基素的合成.
- 评估和比较用于预测批量反应堆中的酶性阿莫西西林生产的数学模型.
- 用实验数据验证动力模型的预测性能.
主要方法:
- 在glyoxyl-agarose上使用固定PGA对阿莫西林的酶合成.
- 开发和评估两个运动模型:迈凯利斯-门模型和一个具有反应/平衡常数的模型.
- 使用马尔科夫链蒙特卡洛 (MCMC) 和遗传算法进行参数估计.
- 使用相对根平均平方误差 (rRMSE) 进行模型性能评估.
主要成果:
- 在使用MCMC进行参数化时,基于Michaelis-Menten的模型在低度的 Ester (rRMSE 1.48%-6.10%) 时显示出优异的预测性能.
- 与基因算法相比,MCMC在这种酶系统中表现出高于基因算法参数估计的精度.
- 经过验证的数学模型准确地预测了半批运行中的酶反应堆的动态行为.
结论:
- 动力建模,特别是使用MCMC,对于通过酶合成优化阿莫西林生产至关重要.
- 精确的动力参数估计提高了酶反应器的可预测性,从而提高了抗生素产量.
- 开发的模型为扩大和控制酶性阿莫西西林制造过程提供了有价值的工具.
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