AI 中文总结
该研究针对免疫系统网络拓扑推断的难题，利用细胞数量数据，构建满足三个特性的模型，提出约束二次规划方法，经实验验证了方法的有效性。
AI 中文摘要
近年来，拓扑推断研究取得了显著进展，该研究有助于阐明众多生物网络中各组件间的相互作用关系。本文聚焦于基于细胞缺失实验收集的数据，推断一组免疫细胞的拓扑结构。该问题极具挑战性，原因在于：i）缺乏适用于细胞相互作用的标准分析模型；ii）实验成本高昂、耗时较长，导致数据获取受限。为解决这些问题，我们首先利用实验的某些常识与观测结果，刻画细胞相互作用过程中细胞数量的三个特性：状态非负性、基于比率的收敛性，以及拓扑权重的三重符号特性。随后，我们构建了一个结构简单、便于分析的新模型，并得出该模型满足上述三个特性的充分条件。最后，基于所构建的模型，我们提出一种约束二次规划方法，以从有限数量的数据对中推断拓扑结构。对实验数据的验证表明了该方法的有效性。
英文摘要
Recent years have witnessed the advanced development of topology inference research, which helps elucidate the interaction relationships of components in many biological networks. This paper focuses on inferring the topology of a group of immune cells, based on the collected data from cell-depletion based experiments. The problem is very challenging due to i) the lack of standard analytical models for the cell interactions, and ii) the restrictive data availability determined by the huge experiment and time costs. To address these issues, we first leverage certain common knowledge and observations on the experiments to characterize three properties on the cell amounts during the interaction process: state non-negativity, ratio-based convergence, and triple signs of topology weights. Then, we construct a new model with simple structure and analytical convenience, and obtain sufficient conditions for the model to accommodate all three properties. Finally, based on the constructed model, we propose a constrained quadratic programming method to infer the topology from limited number of data pairs. Validation on experiment data demonstrate the effectiveness of the proposed method.
CommentsAccepted by IFAC World Congress 2026
