GCAD: a Computational Framework for Mammalian Genetic Program Computer-Aided Design

Area of Science:

• Synthetic biology
• Computational biology
• Mammalian cell engineering

Background:

• Designing genetic programs for mammalian cells is complex due to part limitations and population effects.
• Iterative simulation and experimentation are time-consuming for intricate functions.

Purpose of the Study:

• To develop a computer-aided design framework for mammalian genetic programs.
• To accelerate the design-implementation cycle for synthetic biologists.

Main Methods:

• Developed a genetic algorithm-based framework using a library of characterized parts and dynamical systems models.
• Utilized a directed graph formulation with biologically constrained rules to explore regulatory networks.
• Evaluated framework performance on amplifier, signal conditioner, and pulse generator design problems.

Main Results:

• The framework successfully identified optimal circuit designs for various complexity levels.
• Experimental validation confirmed the feasibility of predicted circuit designs.
• Demonstrated the importance of part characterization for predictive design.

Conclusions:

• The developed framework accelerates the design and implementation of mammalian genetic programs.
• Establishes generalizable approaches for synthetic biology design.
• Highlights the need for computational tools that capture mammalian-specific behaviors and population effects.