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.

翻译：网络推断——即从实验可观测数据中重建复杂系统中相互作用的任务——是系统生物学中一个核心但极具挑战性的问题。尽管过去二十年已取得诸多进展，网络推断仍是一个开放性问题。对于在稳态下观测的系统，由于缺乏时间信息且因果信息丢失，所能获得的洞见有限。获取系统行为因果洞察的两种常见途径是：利用轨迹形式的时间动态信息，以及应用如敲除扰动等干预手段。我们提出一种方法，利用动态和扰动性单细胞数据，联合学习细胞轨迹并增强网络推断能力。该方法受随机动力学的最小熵估计启发，能够从时间标记的单细胞快照中推断有向且有符号的网络。