[针对蛋白质表达系统的机制模型和人工智能模型的研究进展]
Yi Yang1,2, Jun DU1,2, Chunhe Yang1,2
1Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
概括
本综述探讨了蛋白质表达模型,包括基于机制和人工智能的方法. 这些模型有助于理解蛋白质的生产,并优化生物技术应用的序列.
科学领域:
- 分子生物学分子生物学
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 蛋白质表达是一种复杂的,调节的过程,对细胞功能和生物技术至关重要.
- 了解蛋白质表达机制是细胞组织和生物技术进步的关键.
- 目前的模型旨在模拟和预测蛋白质表达动态.
研究的目的:
- 审查最近在机制模型和人工智能预测算法对蛋白质表达的进展.
- 探索蛋白质表达过程的定量模拟.
- 分析影响蛋白质表达的调节因素.
主要方法:
- 对模拟蛋白质表达基本过程的化学反应网络模型的审查.
- 检查人工智能 (AI) 预测算法用于分析监管因素.
- 讨论数据驱动的AI模型用于序列优化.
主要成果:
- 机制模型在数学上描述蛋白质表达,但面临参数确定挑战.
- 人工智能模型专注于序列特征 (蛋白质/DNA) 来预测和优化表达.
- 联合机制和人工智能模型为蛋白质表达提供了更深入的见解.
结论:
- 蛋白质表达模型对于理解细胞调节和推进生物技术至关重要.
- 人工智能和机制模型为研究蛋白质表达提供了互补的方法.
- 综合模型支持高效的高价值蛋白质生产和协调的蛋白质调节.
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