Abstract

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input–output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions.

Highlights

  • A central concept of Synthetic Biology [1] is the rational design of synthetic gene circuits by means of modularized, standard parts, which are DNA traits with well-defined functions

  • We developed an algorithm for automatic circuits design that involves the following basic steps: (i) reading a pre-defined truth table, (ii) converting it into a Karnaugh map, (iii) deriving both POS and SOP circuit expressions, and (iv) implementing a selected solution using standard biological parts and gates

  • We presented a procedure for the automatic design of digital synthetic gene circuits based on standard biological parts

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Summary

Introduction

A central concept of Synthetic Biology [1] is the rational design of synthetic gene circuits by means of modularized, standard parts, which are DNA traits with well-defined functions. De novo design of circuits able to reproduce a target function is not an easy task and its automation represents a major challenge in Synthetic Biology. Similar optimization-based tools like Genetdes [7] and OptCircuit [8] use simulated annealing and mixed integer dynamic optimization, respectively. These approaches yielded interesting circuit designs, but they have several inherent limitations. Computational complexity requires very simplified models that do not represent basic parts but lump functionalities of entire genes.

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