结合动态轨迹与扰动数据，推断细胞系统中的有向因果网络。
Joint trajectory and network inference via reference fitting
- 基于最小熵估计，联合建模单细胞时间序列与扰动数据。
- 从时序单细胞快照中恢复出有向且带符号的调控网络。
- 适用于发育、分化等涉及动态变化的生物系统研究。
网络推断是从实验观测数据重构复杂系统内部相互作用的核心挑战，在系统生物学中尤为关键。尽管过去二十年取得进展，该问题仍未解决。对于稳态观测系统，由于缺乏时间信息，因果关系难以确定。现有方法通常依赖动态轨迹或基因敲除等干预手段获取因果线索。本文提出一种新方法，同时利用动态轨迹与单细胞扰动数据，联合学习细胞演化轨迹并实现网络推断。该方法基于随机动力学的最小熵估计原理，可从带有时间戳的单细胞快照中推断出有向且带符号的网络结构。结果表明，该方法在模拟与真实数据集上均能有效恢复网络拓扑与方向性。
原文摘要 · Abstract (English)
Network inference, the task of reconstructing interactions in a complex system from experimental observables, is a central yet extremely challenging problem in systems biology. While much progress has been made in the last two decades, network inference remains an open problem. For systems observed at steady state, limited insights are available since temporal information is unavailable and thus causal information is lost. Two common avenues for gaining causal insights into system behaviour are to leverage temporal dynamics in the form of trajectories, and to apply interventions such as knock-out perturbations. We propose an approach for leveraging both dynamical and perturbational single cell data to jointly learn cellular trajectories and power network inference. Our approach is motivated by min-entropy estimation for stochastic dynamics and can infer directed and signed networks from time-stamped single cell snapshots.
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