Biology subjects

Myers, C. J.

Publications and source records attributed to Myers, C. J..

5 recordsLinked to original sources

bioRxiv2023

SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post-hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encour-age author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, named entity normalization, and sequence matching, SeqImprove creates machine-readable sequence data and metadata annotations, which authors can then review and edit before sub-mitting a final sequence file. SeqImprove makes it easier for authors to submit FAIR sequence data that is findable, accessible, interoperable, and reusable.

bioinformatics↗

bioRxiv2022

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

Accelerating the development of synthetic biology applications requires reproducible experimental findings. Different standards and repositories exist to exchange experimental data and metadata. However, the associated software tools often do not support a uniform data capture, encoding, and exchange of information. A connection between digital repositories is required to prevent siloing and loss of information. To this end, we developed the Experimental Data Connector (XDC). It captures experimental data and related metadata by encoding it in standard formats and storing the converted data in digital repositories. Experimental data is then uploaded to Flapjack and the metadata to SynBioHub in a consistent manner linking these repositories. This produces complete connected experimental datasets that are exchangeable. The information is captured using a single template Excel Workbook, which can be integrated into existing experimental workflow automation processes. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/520467v1_ufig3.gif" ALT="Figure 3"> View larger version (50K): org.highwire.dtl.DTLVardef@1c5f886org.highwire.dtl.DTLVardef@3f2a6dorg.highwire.dtl.DTLVardef@fa6cb2org.highwire.dtl.DTLVardef@f63236_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa

Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to allow easier use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL, and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin. We illustrate the operation of the Excel-SBOL Converter with two case studies: uploading experimental data with the studys metadata linked to the measurements and downloading the Cello part repository. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/505873v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@c06d7aorg.highwire.dtl.DTLVardef@153724eorg.highwire.dtl.DTLVardef@1758791org.highwire.dtl.DTLVardef@11779c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

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

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.

synthetic biology↗

bioRxiv2022

Investigating And Modeling the Factors that Effects the Performance of Genetic Circuits

Over the past two decades, synthetic biology has yielded ever more complex genetic circuits able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a scale step to the design-build-test workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof-of-concept, we designed and tested a genetic circuit under different temperatures, mediums, inducer concentrations, and bacterial growth phases. We determined that the circuits performance is dramatically altered when these factors differ from the optimal lab conditions. Based on these results, a scaling effort, coupled with a learning process, proceeded to generate model predictions for the genetic circuits performance under untested conditions, which is currently lacking in synthetic biology application design. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on a scale step is needed to ensure correct and robust performances.

synthetic biology↗