Multi-objective design of synthetic biological circuits

Computational methods enable the design of synthetic biological circuits demonstrating a specific dynamic behavior. Current methods are based on the assembly of parts characterized in different contexts, which often fail to operate as predicted when combined. Here we introduce a circuit design method that compensates for parts uncertainty by identifying circuit topologies whose behavior is robust to variations in parameters. Our heuristic topological filtering approach efficiently yields robust circuit designs in a Bayesian framework, and enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation.