Using dynamic model building and simulation to understand cytokines and adaptive immunity 2258020

Abstract

Abstract Introduction An immune response is mediated by a complex interconnected system of cells and molecules. Understanding these responses requires systems-level thinking. Traditional static diagrams in textbooks fail to convey the dynamic interactions between immune components over time. To address this gap, we developed a set of interactive activities using the computational modeling platform Cell Collective. The collection enables students to visualize, predict, and simulate how cytokine receptor binding influences CD4+ T cell differentiation and how Th1 and Th2 subsets shape immune responses to distinct pathogens. Methods The lesson uses scaffolded, hands-on exercises that engage students in computational modeling and simulation to explore CD4+ T cell differentiation and cytokine signaling pathways. The module was implemented in three different immunology-focused undergraduate settings. Students completed pre- and post-assessments to measure learning gains and confidence in computational modeling, along with a post-activity survey evaluating user experience. Results Student knowledge content scores increased from an average of 60.4% pre-lesson to 89.0% post-lesson, indicating significant learning gains. Post-assessment responses revealed that 86% (n = 42) of students agreed that the activity increased their understanding of the key immunological concept that the immune system is interconnected and interacting. Additionally, 89% (n = 36) provided positive feedback regarding their overall experience with the module. Conclusion Integrating computational modeling into undergraduate immunology instruction significantly enhances student understanding of complex, systems-level immune mechanisms. The use of Cell Collective allows learners to move beyond static representations and actively explore how cellular interactions influence immune outcomes. The module is adaptable to various course levels and offers a scalable, evidence-based strategy for improving immunology education through active, technology-enhanced learning. Funding Source This work was supported by the National Science Foundation under Grants #1915131 awarded to Tomas Helikar and RCN-UBE #2316260 awarded to ImmunoReach. Topic Categories Immunology Education and Communication (EDU)