从粗观测结果发现消散:一个随机行走的案例研究,其中没有观察到内部状态
Oleg A Igoshin1, Anatoly B Kolomeisky2, Dmitrii E Makarov3
1Department of Bioengineering, Department of Chemistry, Department of Biosciences, and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA.
The Journal of chemical physics
|January 15, 2025
概括
从粗粒度动力学中估计微观消散可以低估真实值,即使有时间尺度的分离. 马尔科夫近似在特定速率约束下可能产生准确的产量,但当它失败时,需要替代模型.
科学领域:
- 物理 物理学 物理
- 化学 化学 化学
- 生物学 生物学 生物学
- 统计力学 统计力学
- 非平衡的热力学 热力学
背景情况:
- 从低维实验数据中推断显微动力学是具有挑战性的.
- 经常使用像马科维亚主方程这样的介面描述,但由此产生的粗粒度动力学可能是非马科维亚的.
- 由于缓慢和快速过程之间的时间尺度分离,马尔科夫近似经常被应用.
研究的目的:
- 为了调查从粗粒度动力学中消散估计的准确性.
- 确定马尔科夫近似给出确切产生的条件.
- 探索分析非马科夫动态和产生的替代方法.
主要方法:
- 使用了简化的分子电机模型,并未观察到内部状态.
- 分析了时间尺度分离对散射估计的影响.
- 评估了马尔科夫近似在不同微观速率约束下产生的准确性.
- 计算Kullback-Leibler分歧用于轨迹分析.
- 被认为是隐藏的马尔科夫模型来揭示消散动力学.
主要成果:
- 从粗略动力学的散射估计可以显著低估显微散射,即使有时间尺度的分离.
- 时间尺度的分离并不总是必要的马尔科夫近似提供精确的产量.
- 通过库尔巴克-莱布勒分歧将记忆效应纳入无模型,使产量估计恶化.
- 隐藏的马尔科夫模型可以揭示消散的微观动力学,即使是从时间可逆的粗轨迹.
结论:
- 马尔科夫近似对于生成的有效性取决于微观速率约束,而不仅仅是时间尺度的分离.
- 标准的粗粒加工方法可能会掩盖必要的微观消散.
- 像隐藏的马尔科夫模型这样的先进技术对于准确地描述复杂的非平衡系统至关重要.
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