通过机器学习,数学和代谢网络建模阿斯塔丁生物合成的建模
Vinoj Chamilka Liyanaarachchi1, Gannoru Kankanamalage Sanuji Hasara Nishshanka1, P H Viraj Nimarshana2
1Department of Chemical and Process Engineering, Faculty of Engineering, University of Moratuwa, Moratuwa, Sri Lanka.
Critical reviews in biotechnology
|August 16, 2023
概括
数学建模通过模拟生物系统来帮助阿斯塔克桑的生产. 这种方法有助于优化产量并降低各种生物体中天然阿斯丁 (NA) 合成的成本.
科学领域:
- 生物技术是生物技术.
- 生物化学工程 生物化学工程
- 系统生物学 系统生物学
背景情况:
- 天然素 (NA) 是一种有价值的抗氧化剂,由各种生物合成,包括微藻,细菌,真菌和植物.
- 目前对NA生物合成和积累因子的理解有限,这使得通过实验操纵来优化产量变得复杂和昂贵.
- 预测建模为理解和增强NA生产提供了传统实验方法的有希望的替代方案.
研究的目的:
- 综合审查现有的数学建模技术来模拟阿斯塔克桑生物积累.
- 分析应用这些模型的挑战,解决方案和未来前景.
- 为优化NA生产提供洞察力,以高效和具有成本效益的方式.
主要方法:
- 对阿斯塔克桑生物合成应用的数学建模方法的文献综述.
- 对专注于预测各种生物系统中阿斯塔克桑生产动态的研究进行分析.
- 在建模阿斯塔丁积累方面,对挑战和建议解决方案进行批判性评估.
主要成果:
- 数学模型可以预测阿斯塔丁生产动态,有助于过程理解.
- 建模可以估计生物质和阿斯塔克桑产量的最佳条件.
- 基于模拟的方法可以显著减少与实验优化相关的时间和成本.
结论:
- 数学建模和模拟是理解和优化天然阿斯丁生物合成的强大工具.
- 这些技术为更高效和更具成本效益的阿斯塔山丁生产提供了途径.
- 进一步开发和应用建模对于推进阿斯塔克桑生产技术至关重要.
相关概念视频
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