Aquarium: open-source laboratory software for design, execution and data management

Vrana, Justin D.; Lange, Orlando de; Yang, Yaoyu; Newman, G. M.; Saleem, Ayesha; Miller, Abraham; Cordray, Cameron; Halabiya, Samer; Parks, Michelle; Lopez, Eriberto; Goldberg, Sarah; Keller, Benjamin J.; Strickland, Devin; Klavins, Eric

doi:10.1093/synbio/ysab006

Cited by 24 publications

(31 citation statements)

References 27 publications

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“…Examples include methods for DNA sequence design of genetic parts (6; 7), for parts choice and construction into a linear DNA sequence (8; 9; 10), for landing pad choice (11), and for circuit structure or topology (12). Experimental protocols are generally custom designed for each application, but tools exist to facilitate the reproducibility of protocols (13; 14). Some tools integrate several stages of the design process together; for example, the software tool Cello (15; 16) takes a logic function written in the Verilog language and identifies a single circuit structure using design principles from electronic circuit design that employ NOT and NOR gates.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…The design/prediction tools are Dynamic Signatures Generated by Regulatory Networks (DSGRN, (20; 29; 30)) and Combinatorial Design Model (CDM (31; 32)). The build tools are DNA Assembly (DASi, (33; 34; 35)), the computer-aided process planning tool Terrarium (36; 35), and the lab software Aquarium (14; 37).…”

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

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“…While most LIMS applications have been designed as enterprise software products designed for corporate laboratories, the synthetic biology community has developed software tools supporting its data-driven research workflows [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…They can be used to manage inventories, Standard Operating Procedures, data produced by instruments, and more advanced workflows. While most LIMS applications have been designed as enterprise software products designed for corporate laboratories, the synthetic biology community has developed software tools supporting its data-driven research workflows [24, 25].…”

Section: Introductionmentioning

confidence: 99%

Organizing laboratory information to analyze the reproducibility of experimental workflows

Peccoud

Johnson²,

Peccoud³

et al. 2022

Preprint

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Reproducibility is the cornerstone of scientific experiments. Assessing the reproducibility of an experiment requires analyzing the contribution of different factors to the variation of the observed data. Suitable data structures need to be defined prior to the data collection effort so that data associated with these factors can be recorded and associated with observations of the variable of interest. The resulting datasets can be analyzed statistically to estimate the effect of experimental factors on the observed data using ANOVA models. Custom data structures to document the execution of experimental workflows are defined in a research data management system. The data produced by multiple repetitions of a plasmid purification process and a cell culture process are analyzed using the Kruskal–Wallis H-test to identify factors contributing to their variation. Repetitions of the plasmid purification process do not lead to significant differences in extraction yields. Statistically significant differences in plasmid solution purity are identified but the differences are small enough that are not biologically relevant. The maintenance of two cell lines over many generations leads to similar datasets. However, different media preparations appear to influence the variation of cell viability and harvested cell counts in unexpected ways that may be the indirect expression of hidden effects not captured in the data structure.

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“…The Aquarium project, for example, offers a special language to describe replicable laboratory instructions and generates step-by-step guides to ensure that protocols will be run the same way every time. 4 …”

Section: Introductionmentioning

confidence: 99%

Pipette Show: An Open Source Web Application to Support Pipetting into Microplates

2022

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Despite increasing automation, manual pipetting remains a daily important task in life science laboratories. However, the creation of an efficient work plan is often time-consuming, and its completion is error-prone. Here, we present Pipette Show, a free Vue.js based application that optimizes the generation of an efficient work plan for pipetting into microplates and supports its reliable execution by visual guidance. The basis forms a graphical web interface with a module for building workflows as well as a module displaying the information for each pipetting step by illuminating wells of microplates placed on a tablet.

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Aquarium: open-source laboratory software for design, execution and data management

Cited by 24 publications

References 27 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

Organizing laboratory information to analyze the reproducibility of experimental workflows

Pipette Show: An Open Source Web Application to Support Pipetting into Microplates

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