Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Abstract

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (Nielsen, A., et al., Science , 2016 , 352 (6281), DOI: ). Across this set of functions, 32 of them can be improved by simply considering structural variants yielding performance gains of up to 7.9-fold, whereas 22 of them can be improved with gains up to 26-fold when selecting circuits according to the novel robustness score. We furthermore report on the synergistic combination of the two proposed improvements.

Context-Aware Technology Mapping in Genetic Design Automation

“…For example, the GDA tool Cello uses a circuit score based on the separation of the complementary Boolean outputs . It has been shown that the circuit topology plays an important role in genetic circuit design . Yet, the computationally challenging part of circuit design is finding an assignment of genetic gates from a library to the topology.…”

“… 25 It has been shown that the circuit topology plays an important role in genetic circuit design. 19 Yet, the computationally challenging part of circuit design is finding an assignment of genetic gates from a library to the topology. Each single assignment needs to be evaluated by simulating the circuit and determining its score, making the gate assignment a combinatorial optimization problem.…”

“…The γ and S used are given in the Supporting Information , but five libraries are visualized in Figure 4 A–E. Additionally, the set of 33 circuits is extended with structural variants synthesized in ref ( 19 ) to form a set of 66 circuits. We could observe that for low to moderate total intensity in crosstalk only a small deterioration in either score of the found assignment or number of simulations w.r.t.…”

“…Hence, single-cell data for parts or circuits can be used to learn or calibrate context models. In turn, GDA tools incorporating context effects can then evaluate and score a circuit candidate based on its generated single-cell data across an in silico cell population. , This provides means to identify circuit candidates or additional regulatory motifs leading to robust circuit behavior in the presence of context effects.…”

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

“…Cello then optimizes the modular construction of the logic circuit from a parts library to create a linear DNA sequence with the desired circuit. Another method ( 17 ) exhaustively explores fan-out free circuit structures and jointly optimizes structure and parts assignment for a specific logic function.…”