Biology subjects

Roehner, N.

Publications and source records attributed to Roehner, N..

7 recordsLinked to original sources

bioRxiv2026

Developing a Standard Definition for Sequences of Concern

Readily available nucleic acid synthesis is both critical for the bioeconomy and an increasingly pressing security concern due to the potential for accidental or deliberate misuse. While biosecurity experts broadly agree that nucleic acid providers should screen orders for potential "sequences of concern," there has previously been no agreed standard for how to define and recognize such sequences. To address this gap, we first organized a test set of 1.1 million sequences from pathogens and toxins on the Australia Group Common Control Lists and their non-controlled relatives, along with model organisms and synthetic constructs. An initial categorization of sequences as to whether or not they were sequence of concern was produced by comparing the results of four biosecurity screening systems for each of these sequences, finding that these systems already agreed on the categorization of more than 80% of sequences. We then refined these results through a science-based stakeholder review process to define a rubric for determining whether a sequence should be flagged as a potential sequence of concern, then applied this rubric to improve the categorization of test sets. The result is a rubric that identifies sequences of concern with respect to human pandemic-potential viruses, key classes of low-risk genes, and controlled toxins. Applying this rubric to the test set collection has reduced the number of test sequences with disputed categorization by 44.3% for controlled viruses and 10.7% across the test set as a whole. Together, these results provide a concrete "sequence of concern" definition that can be used as a foundation for development of biosecurity screening standards and policy.

bioinformatics↗

bioRxiv2022

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

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.

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

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

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al. Our experimental campaign used a novel high throughput experimentation framework developed under DARPAs Synergistic Discovery and Design (SD2) program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report, and make some remarks about lessons learned.

synthetic biology↗

bioRxiv2021

Data Representation in the DARPA SD2 Program

1Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems [6, 15], but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves [7]. The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing. The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases (Figure 1(a)). This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants (Figure 1(b)). This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration (Figure 1(c)), enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/460644v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@fc7371org.highwire.dtl.DTLVardef@1ff2733org.highwire.dtl.DTLVardef@66af53org.highwire.dtl.DTLVardef@1809640_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Architectures for data sharing: bilateral relations (a), command and control (b), and flexible rendezvous (c). C_FIG

synthetic biology↗

bioRxiv2020

Verification of genetic engineering in yeasts with nanopore whole genome sequencing

Yeast genomes can be assembled from sequencing data, but genome integrations and episomal plasmids often fail to be resolved with accuracy, completeness, and contiguity. Resolution of these features is critical for many synthetic biology applications, including strain quality control and identifying engineering in unknown samples. Here, we report an integrated workflow, named Prymetime, that uses sequencing reads from inexpensive NGS platforms, assembly and error correction software, and a list of synthetic biology parts to achieve accurate whole genome sequences of yeasts with engineering annotated. To build the workflow, we first determined which sequencing methods and software packages returned an accurate, complete, and contiguous genome of an engineered S. cerevisiae strain with two similar plasmids and an integrated pathway. We then developed a sequence feature annotation step that labels synthetic biology parts from a standard list of yeast engineering sequences or from a custom sequence list. We validated the workflow by sequencing a collection of 15 engineered yeasts built from different parent S. cerevisiae and nonconventional yeast strains. We show that each integrated pathway and episomal plasmid can be correctly assembled and annotated, even in strains that have part repeats and multiple similar plasmids. Interestingly, Prymetime was able to identify deletions and unintended integrations that were subsequently confirmed by other methods. Furthermore, the whole genomes are accurate, complete, and contiguous. To illustrate this clearly, we used a publicly available S. cerevisiae CEN.PK113 reference genome and the accompanying reads to show that a Prymetime genome assembly is equivalent to the reference using several standard metrics. Finally, we used Prymetime to resequence the nonconventional yeasts Y. lipolytica Po1f and K. phaffii CBS 7435, producing an improved genome assembly for each strain. Thus, our workflow can achieve accurate, complete, and contiguous whole genome sequences of yeast strains before and after engineering. Therefore, Prymetime enables NGS-based strain quality control through assembly and identification of engineering features.

synthetic biology↗

bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗