使用DMRG进行化学总方程参数探索
John P Zima1, Schuyler B Nicholson1, Todd R Gingrich1
1Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA.
The Journal of chemical physics
|August 7, 2025
概括
张量网络近似化学反应网络分布,在基因切换开关模型中揭示了双稳定性. 该方法捕获超出平均场分析的随机波动,用于更广泛的CRN应用.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 系统生物学 系统生物学
背景情况:
- 化学反应网络 (CRN) 具有复杂的动态,与相关的物种波动相关.
- 蒙特卡洛方法是分析CRN随机轨迹的标准.
- 使用张量网络在大型状态空间上近似的联合分布是一个新兴的领域.
研究的目的:
- 应用张量网络方法来分析七种基因切换开关CRN的稳定状态分布.
- 在CRN模型中确定双稳定性的条件作为分解速率的函数.
- 为了证明张量网络在捕获随机效应和实现参数探索方面的能力.
主要方法:
- 使用张量网络策略,特别是密度矩阵重规范化组 (DMRG) 算法,来计算稳定状态分布.
- 分析七个物种的基因切换开关CRN模型.
- 使用兴奋状态DMRG来计算放松时间表.
主要成果:
- 张量网络方法成功确定了基因切换开关CRN的稳定状态分布.
- 双稳定性被确定为两个分解速率的函数,张量网络捕捉了超出平均场近似的随机效应.
- 该方法证明了对订单参数进行有效投射以识别双模性,并计算了放松时间表.
结论:
- 张量网络为计算CRN的稳态分布提供了强大的工具,为随机动态提供了洞察力.
- 这种方法对具有显著波动的系统的准确性超过了平均场分析.
- 开发的技术有助于参数探索和分析复杂的CRN模型,包括可比式系统.
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