Topology Inference for Immune System Networks by Using Cell Amount Data

2026-08-07

AI Summary

This study addresses the challenge of scarce data and the absence of standardized analytical models in immune cell depletion experiments by proposing an integrative approach that combines biological prior knowledge with dynamical modeling and topological inference. Leveraging key biological constraints—non-negativity of cell population dynamics, convergence of cellular ratios, and sign constraints on interaction effects—the authors construct a parsimonious analytical model and develop a constrained quadratic programming algorithm to infer immune network topology from limited observational data in an interpretable manner. Validation on real experimental data demonstrates that the method effectively recovers biologically plausible intercellular interaction structures. Notably, this work is the first to formally encode and integrate these three fundamental dynamical properties into a small-sample network reconstruction framework.

Abstract

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.

Problem

• topology inference
• immune system networks
• cell amount data
• cell-depletion experiments
• interaction relationships

Innovation

• topology inference
• immune system networks
• constrained quadratic programming
• cell interaction modeling
• data-scarce inference