Modeling Gene Circuit Dynamics: An Educational Toolkit Integrating Modular Hardware and Predictive Software
2025 AIChE Annual Meeting
Modeling Gene Circuit Dynamics: An Educational Toolkit Integrating Modular Hardware and Predictive Software
Advances in synthetic biology rely on understanding how genetic circuits translate molecular interactions into dynamic cellular behavior. However, existing simulation platforms are often inaccessible to early learners or lack integration with physical systems. To address this, we developed MAthematical Gene Circuit (MAGiC) Modeling kits, which include a physical plug-and-play CellBoard along with a paired software interface, to serve as an educational toolkit that makes gene circuit dynamics both visible and interactive.
The software is built on a Flask-based web framework and features a drag-and-drop interface where users assemble genetic parts of promoters, ribosome binding sites, coding sequences, and terminators into functional circuits. Each design is automatically translated into a system of ordinary differential equations that capture transcriptional and translational dynamics based on component layout and regulatory logic. These equations are solved in real time using SciPy’s ODE integrators, and the resulting expression profiles are displayed as interactive plots that reveal temporal changes in protein concentration. The CellBoard hardware mirrors this workflow through modular, tangible parts that represent genetic elements, enabling users to construct and test circuits physically while observing corresponding simulated behavior. This integration of physical and digital design provides immediate, intuitive feedback on circuit function, allowing users to directly visualize regulatory logic such as activation and repression.
Together, MAGiC kits create an open-source environment for teaching genetic regulation, system dynamics, and computational modeling. This work demonstrates how accessible, interactive platforms can transform abstract quantitative models into intuitive learning experiences, enhancing understanding of synthetic biology and broadening participation in computational biology education.