Inferring Factor Importance with Explainable AI Model for Macrophage Immune Cell Polarization

Abstract

Macrophages are highly plastic immune cells that dynamically shift between pro- and anti-inflammatory phenotypes in response to cytokines, transcriptional regulators, metabolic cues, and local microenvironment. Yet macrophage polarization is governed by nonlinear regulatory networks that are difficult to study experimentally. Here, we integrate mechanistic simulation, surrogate neural networks, and explainable machine learning to identify key regulatory drivers of polarization. We generated simulation data with a COPASI-based macrophage polarization model and used them to train an LSTM surrogate capable of reconstructing temporal macrophage marker dynamics across diverse macrophage activation states. SHAP, Individual Conditional Expectation, and Partial Dependence Plot analyses quantified feature importance, identified nonlinear response regimes, and characterized higher-order interactions among cytokines, transcription factors, and polarization markers. The model identified oxygen availability and IL10 as dominant anti-inflammatory regulators, consistently promoting M2-associated outputs such as ARG1 and VEGF. Conversely, IFN-γ, TNFα, IL12, iNOS, CXCL9, and mCXCL10 reinforced M1-associated inflammatory programs. STAT1-centered analyses identified iNOS, TNFα, and mCXCL10 levels as strong contributors to STAT1 outcomes, consistent with amplification of the IFN- γ/JAK/STAT1 inflammatory axis. VEGF negatively contributed to predicted STAT1 output, suggesting antagonism between M2 markers and M1 activation. SHAP, ICE, and PDP analyses also showed that STAT1 regulation is nonlinear and threshold-like: pro-inflammatory signals must accumulate before STAT1 rises sharply. Together, it demonstrates that the LSTM explainability framework can identify polarization drivers, uncover nonlinear regulatory thresholds, and prioritize pathways for experimental validation.