ArticleBriefings in bioinformatics2026
Integrative agent-based modeling of cutaneous lupus: single-cell benchmarking and in silico evaluation of B-cell therapies.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Agent-based models (ABMs) offer a powerful framework for representing emergent immune dynamics, yet their integration with single-cell datasets and therapeutics simulation remains limited. Here, we presented an integrative, single-cell informed systems immunology modeling framework built on universal immune system simulator (UISS) to mechanistically represent autoimmune skin inflammation specifically systemic and cutaneous lupus erythematosus. The framework incorporates (i) data-driven initialization from lesion single-cell RNA sequence cell fractions, (ii) modular immune processes coupling type I interferon signaling, ultraviolet induced keratinocyte injury, and (iii) a digital patient generation pipeline that captures inter-individual variability through stochastic parameter perturbation and quality-controlled cohort assembly. Using this platform, we constructed a cohort of 50 digital patients initially and implemented modular therapy components for B-cell Activating Factor (BAFF) neutralization (belimumab) and anti-CD20 B-cell depletion (rituximab) . In silico, pre-post treatment experiments revealed heterogeneous serologic responses, modest median reductions in immunoglobulin G antibodies that target the Ro/SSA protein (Ro-reactive IgG), and strong correlations between plasmablast dynamics and circulation autoantibodies, highlighting the mechanistic constraints on B-cell targeted therapies. Overall, our results demonstrated how single-cell benchmarking, rule-based immune modeling, and digital patient ensembles can be integrated into a generalizable computational workflow for studying autoimmune pathophysiology and simulating therapeutic mechanisms to explore mechanistic determinants of disease progression and therapeutic response.
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