Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

ABSTRACT

Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.