Active learning about the dynamic and complex nature of CD4+ T cell differentiation with computer network modeling and simulations

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Abstract CD4+ T cells provide cell-mediated immunity in response to various antigens. During an immune response, naïve CD4+ T cells differentiate into specialized effector T helper cells (e.g., Th-1,-2,-17, etc.), and induced regulatory (iTreg) T cells. Their proper response to pathogens is governed by complex, non-linear dynamic networks consisting of various types of cells and their associated communication mediators (cytokines). The regulation of CD4+ T cell differentiation is a major topic of many post-secondary immunology-related courses. However, the traditional lecture and textbook-based learning format in these (and other life sciences) courses makes it challenging for students to fully conceptualize and appreciate the complex and dynamic nature of such processes. We have developed a new learning approach that enables students to build, simulate, and investigate computational models of processes embedded in biological systems. This method is facilitated through an easy-to-use software, Cell Collective (https://cellcollective.org), that makes computational modeling accessible to any student and instructor (i.e., no prior computational modeling experience is necessary). The simulation lessons have been designed to be turn-key, self-contained, and adoptable by any instructor without the need to modify their syllabus, and with a minimal learning curve. They can be used in-class, assigned as homework, as well as deployed as extensive lab investigations. Here, we will present a new modeling and simulation lesson that enables students to learn about the dynamic complexities of CD4+ T cell differentiation, such as the effects of different cytokines and their dosage on the differentiation outcome, feedback loops, etc.