Abstract: Can we scale up biological circuit designs using CAD tools like we do for digital circuits? How would computer science education change if that were the case? Computational tools in research and education have immense potential to unlock new discoveries and provide a more engaging and inclusive learning experience. For bioengineering research, designing and analyzing biological systems at a larger scale is limited by the heuristic tuning of components to work in specific conditions. To address this, I will present scalable computational tools for (1) writing formal system specifications using assume-guarantee contracts, (2) building mathematical models from graph descriptions, and (3) learning from data using Bayesian inference. With compelling examples from the field of bioengineering, I will introduce a full-stack pipeline of modeling, analysis, and parameter learning aimed at guiding the modeling and design of genetic circuits. In the second part of the talk, I will highlight the educational impact of these tools, showcasing how they can drive a more inclusive, active, and project-based learning in computer science classes.