Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

Goldman, Robert P.; Moseley, Robert C.; Roehner, Nicholas; Cummins, Bree; Vrana, Justin D.; Clowers, Katie J.; Bryce, Daniel; Beal, Jacob; DeHaven, Matthew; Nowak, Joshua; Higa, Trissha; Biggers, Vanessa; Lee, Peter L.; Hunt, Jay D.; Haase, Steven B.; Weston, Mark; Zheng, George; Deckard, Anastasia; Gopaulakrishnan, Shweta; Stubbs, Joseph F.; Gaffney, Niall; Vaughn, Matthew; Maheshri, Narendra; Mikhalev, Ekaterina; Bartley, Bryan; Markeloff, Richard; Mitchell, Tom; Nguyen, Tramy; Sumorok, Daniel; Walczak, Nicholas; Myers, Chris J.; Zundel, Zach; Hatch, Benjamin; Scholz, James; Colonna-Romano, John

doi:10.1101/2022.05.31.493627

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The performance of a circuit is evaluated as a circuit’s ability to express fluorescence (ON) or not (OFF) as intended by the circuit’s logic given the presence or absence of chemical inputs. Logic circuits designed to exhibit OR and NOR logic were chosen based on preliminary data analysis in which previously built OR and NOR circuits performed poorly ((4; 39) and Fig. 6 in (25)).…”

Section: Introductionmentioning

confidence: 99%

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

Cummins

Vrana²,

Moseley

et al. 2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Cummins

Vrana²,

Moseley

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Resultsmentioning

confidence: 99%

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

Cummins

Moseley

Deckard³

et al. 2022

Preprint

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A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

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“…Such laboratories enable the full automation of the design-make-test-analyze (DMTA) cycle [35]. Some examples of automated DMTA have shown promise in protein engineering [36], synthetic biology [15], and chemical synthesis [14]. The coupling of automated data generation and AI-driven experimentation may introduce new paradigms of automated experimentation with no reliance on "man-in-the-loop" [36].…”

Section: Introductionmentioning

confidence: 99%

Prospective Validation of HydraScreen: Virtual Screening and Hit Identification in IRAK1

Kamuntavičius,

Prat,

Paquet

et al. 2023

Preprint

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In this proof-of-concept study, we combine Ro5's digital drug discovery platforms SpectraView and HydraScreen with Strateos' robotic cloud labs capabilities to augment and accelerate target and hit identification. Using SpectraView to select IRAK1 as the target, we prospectively validate HydraScreen, a structure-based deep learning model. We demonstrate that HydraScreen could identify up to 23.8% of all hit compounds by screening only 1% of the compound library, simultaneously identifying the three of the most potent (nanomolar) scaffolds present in the library. All three nanomolar scaffolds identified in our project are novel for IRAK1 and lend themselves for future development. HydraScreen outperforms traditional methods in an unbiased prospective evaluation and offers advanced features such as ligand pose confidence scoring. Thus, SpectraView and HydraScreen are innovative tools which can aid and expedite the stages of early drug discovery.

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Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

Cited by 3 publications

References 17 publications

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

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

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

Prospective Validation of HydraScreen: Virtual Screening and Hit Identification in IRAK1

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