Better research by efficient sharing: evaluation of free management platforms for synthetic biology designs

GMOCU: Digital Documentation, Management, and Biological Risk Assessment of Genetic Parts

The continuous evolution of molecular biology and gene synthesis methods paired with an ever-increasing potential of synthetic biology approaches and genome engineering toolkits enables the rapid design of genetic bioparts and genetically modified organisms. Although various software solutions assist with specific design tasks and challenges, lab internal documentation and ensuring compliance with governmental regulations on biosafety assessment of the generated organisms remain the responsibility of individual academic researchers. This results in inconsistent and redundant documentation regimes and a significant time and labor burden. GMOCU (GMO documentation) is a standardized semi-automatic user-oriented software approach -written in Python and freely available- that unifies lab internal data documentation on genetic parts and genetically modified organisms (GMOs). It automatizes biological risk evaluations and maintains a shared up-to-date inventory of bioparts for team-wide data navigation and sharing. GMOCU further enables data export into customizable formats suitable for scientific publications, official biosafety documents, and the research community.

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 facilitate the 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.

SynBio2Easy-a biologist-friendly tool for batch operations on SBOL designs with Excel inputs

Practical delivery of Findable, Accessible, Reusable and Interoperable principles for research data management requires expertise, time resource, (meta)data standards and formats, software tools and public repositories. The Synthetic Biology Open Language (SBOL2) metadata standard enables FAIR sharing of the designs of synthetic biology constructs, notably in the repository of the SynBioHub platform. Large libraries of such constructs are increasingly easy to produce in practice, for example, in DNA foundries. However, manual curation of the equivalent libraries of designs remains cumbersome for a typical lab researcher, creating a barrier to data sharing. Here, we present a simple tool SynBio2Easy, which streamlines and automates operations on multiple Synthetic Biology Open Language (SBOL) designs using Microsoft Excel® tables as metadata inputs. The tool provides several utilities for manipulation of SBOL documents and interaction with SynBioHub: for example, generation of a library of plasmids based on an original design template, bulk deposition into SynBioHub, or annotation of existing SBOL component definitions with notes and authorship information. The tool was used to generate and deposit a collection of 3661 cyanobacterium Synechocystis plasmids into the public SynBioHub repository. In the process of developing the software and uploading these data, we evaluated some aspects of the SynBioHub platform and SBOL ecosystem, and we discuss proposals for improvement that could benefit the user community. With software such as SynBio2Easy, we aim to deliver a user-driven tooling to make FAIR a reality at all stages of the project lifecycle in synthetic biology research. Graphical Abstract.

Data sharing policies: share well and you shall be rewarded

Sharing research data is an integral part of the scientific publishing process. By sharing data, authors enable their readers to use their results in a way that the textual description of the results does not allow by itself. In order to achieve this objective, data should be shared in a way that makes it as easy as possible for readers to import them in computer software where they can be viewed, manipulated and analyzed. Many authors and reviewers seem to misunderstand the purpose of the data sharing policies developed by journals. Rather than being an administrative burden that authors should comply with to get published, the objective of these policies is to help authors maximize the impact of their work by allowing other members of the scientific community to build upon it. Authors and reviewers need to understand the purpose of data sharing policies to assist editors and publishers in their efforts to ensure that every article published complies with them.

Synthetic Biology Knowledge System

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

Sustainable Chemistry - An Interdisciplinary Matrix Approach

Within the framework of green chemistry, the continuous development of new and advanced tools for sustainable synthesis is essential. For this, multi-facetted underlying demands pose inherent challenges to individual chemical disciplines. As a solution, both interdisciplinary technology screening and research can enhance the possibility for groundbreaking innovation. To illustrate the stages from discovery to the implementing of combined technologies, a SusChem matrix model is proposed inspired by natural product biosynthesis. The model describes a multi-dimensional and dynamic exploratory space where necessary interaction is exclusively provided and guided by sustainable themes.

Responsible innovation in synthetic biology in response to COVID-19: the role of data positionality

Synthetic biology, as an engineering approach to biological systems, has the potential to disruptively innovate the development of vaccines, therapeutics, and diagnostics. Data accessibility and differences in data-usage capabilities are important factors in shaping this innovation landscape. In this paper, the data that underpin synthetic biology responses to the COVID-19 pandemic are analyzed as positional information goods-goods whose value depends on exclusivity. The positionality of biological data impacts the ability to guide innovations toward societally preferred goals. From both an ethical and economic point of view, positionality can lead to suboptimal as well as beneficial situations. When aiming for responsible innovation (i.e. embedding societal deliberation in the innovation process), it is important to consider hurdles and facilitators in data access and use. Central governance and knowledge commons provide routes to mitigate the negative effects of data positionality.