在ErbB信号网络中的多尺度建模中的网络结构和参数.
Ai Shinobu1, Ayaka Nagasato-Ichikawa2, Mariko Okada3
1Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), The University of Osaka, Suita, Osaka 565-0871, Japan.
Current opinion in cell biology
|December 11, 2025
概括
本综述探讨了用于信号传导的细胞和分子建模,重点关注ErbB系统. 整合这些方法可以提高疾病过程的理解,尽管存在数据挑战.
科学领域:
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
- 生物物理学的生物物理.
背景情况:
- 信号转导控制了健康和疾病中的细胞行为.
- 细胞建模有助于重建疾病动态.
- 人工智能有先进的模型生成,但参数估计在有限的数据下是困难的.
研究的目的:
- 审查ErbB信号系统建模方面的进展.
- 突出分子和细胞建模范式的整合.
- 讨论计算生物学中的新兴趋势.
主要方法:
- 系统生物学方法的审查.
- 对分子动力学模拟的分析.
- 检查人工智能辅助的模型生成.
主要成果:
- 分子动力学模拟提供了高分辨率的洞察力,但具有可扩展性的限制.
- 在数据限制下,参数估计具有挑战性.
- 最近的进展集中在细胞和分子尺度上的ErbB信号传输上.
结论:
- 整合分子和细胞建模对于理解复杂的生物系统至关重要.
- 像模拟数据重用和机器学习这样的新兴趋势正在推动这种整合.
- 在现实的环境环境中建模是一个关键的未来方向.
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