Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares
Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares
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DOI：
10.1103/physreve.109.034301
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发表时间：
2024-03-04
影响因子：
2.4
通讯作者：
Magdon-Ismail，Malik
中科院分区：
文献类型：
--
作者：
Ding，Yanna;Gao，Jianxi;Magdon-Ismail，Malik
在网络动力系统中，推断控制参数对于预测节点动态至关重要，例如基因表达水平、物种丰度或种群密度。虽然许多参数估计技术依赖于时间序列数据，特别是在极端时间范围内收敛的系统，但只有嘈杂的稳态数据可用，这需要一种新的方法来从稳态的嘈杂观测中推断动态参数。然而，传统的优化过程计算量大，需要反复模拟耦合常微分方程。为了克服这些限制，我们引入了一个替代目标函数，该函数利用解耦方程来计算稳态，大大降低了计算复杂性。此外，通过优化代理目标函数，我们获得了比噪声观测更准确地接近地面真相的稳态，并预测了拓扑变化时的未来平衡态。我们通过经验证明了所提出的方法在生态，基因调控和流行病网络中的有效性。我们的方法提供了一种从稳态数据中估计参数的有效方法，并有可能改善网络动力系统的预测。
In networked dynamical systems, inferring governing parameters is crucial for predicting nodal dynamics, such as gene expression levels, species abundance, or population density. While many parameter estimation techniques rely on time-series data, particularly systems that converge over extreme time ranges, only noisy steady-state data is available, requiring a new approach to infer dynamical parameters from noisy observations of steady states. However, the traditional optimization process is computationally demanding, requiring repeated simulation of coupled ordinary differential equations. To overcome these limitations, we introduce a surrogate objective function that leverages decoupled equations to compute steady states, significantly reducing computational complexity. Furthermore, by optimizing the surrogate objective function, we obtain steady states that more accurately approximate the ground truth than noisy observations and predict future equilibria when topology changes. We empirically demonstrate the effectiveness of the proposed method across ecological, gene regulatory, and epidemic networks. Our approach provides an efficient and effective way to estimate parameters from steady-state data and has the potential to improve predictions in networked dynamical systems.