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

Abstract

Computational tools addressing various components of design-build-test-learn loops (DBTL) for the construction of synthetic genetic networks exist, but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called DART (Design Assemble Round Trip). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection, and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

“…The inclusion of CRISPR reduced transcriptional leakage under repression. Each logic function was composed of four strains, one for each Boolean input state (00, 01, 10,11), where the expression of the input gRNA was controlled by the presence or absence of the associated promoter. Various combinations of the CRISPR NOR gates were constructed for each logic circuit.…”

“…XNOR and AND (the best performer according to Wasserstein cut scoring) have the least overlap between ON and OFF distributions; however, their medians are not well-separated. [11,[13][14][15] on flow cytometry data. The strain labeled UWBF_6390 in the bottom panel corresponds to the NOR 00 strain.…”

“…With further examination, it was observed that the provenance of the OR strains is not clear, and that there may have been an error in the labeling or construction of one or more OR strains. This is backed by inconsistencies between the reported strain sequence, the sequence uploaded to SynBioHub [11], DNAseq [16], and small RNAseq experiments.…”

“…We use the idea of a graph cut [21] to produce a score to rank-order the fit of truth tables to the data , , , and . We partition the nodes of the edge-weighted graph in Figure 6 to each That partition corresponds to two truth tables, T and NOT T. For example, the partition [00] and [01, 10,11] means that one set has an expected output value of 1 (ON) and the other has the expected value 0 (OFF). If 1 is assigned to the partition [00], then we have the NOR circuit, where fluorescence should be produced only if both inputs are absent.…”

“…Figure 4: Circuit performance using the Performance Metrics (top) and Data Diagnosis (bottom) analysis packages[11,[13][14][15] on flow cytometry data. The strain labeled UWBF_6390 in the bottom panel corresponds to the NOR 00 strain.…”