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

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.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

A synthetic biology workflow covers the roadmap from conceptualization of a genetic device to its construction and measurement. It is composed of databases that provide DNA parts/plasmids, wet-lab methods , software tools to design circuits, simulation packages , and tools to analyze circuit performance. The interdisciplinary nature of such a workflow requires that experimental results and their in-silico counterparts proceed alongside, with constant feedback between them. We present an end-to-end use case for engineering a simple synthetic device, where information standards maintain coherence throughout the workflow. These are the Standard European Vector Architecture (SEVA), the Synthetic Biology Open Language (SBOL), and the Systems Biology Markup Language (SBML).

Standards play a crucial role in ensuring consistency, interoperability, and efficiency of communication across various disciplines. In the field of synthetic biology, the Synthetic Biology Open Language (SBOL) Visual standard was introduced in 2013 to establish a structured framework for visually representing genetic designs. Over the past decade, SBOL Visual has evolved from a simple set of 21 glyphs into a comprehensive diagrammatic language for biological designs. This perspective reflects on the first ten years of SBOL Visual, tracing its evolution from inception to version 3.0. We examine the standard's adoption over time, highlighting its growing use in scientific publications, the development of supporting visualization tools, and ongoing efforts to enhance clarity and accessibility in communicating genetic design information. While trends in adoption show steady increases, achieving full compliance and use of best practices will require additional efforts. Looking ahead, the continued refinement of SBOL Visual and broader community engagement will be essential to ensuring its long-term value as the field of synthetic biology develops.

The Synthetic Biology Open Language (SBOL) is a community-driven open language to promote standardization in synthetic biology. To support the use of SBOL in metabolic engineering, we developed SBOLme, the first open-access repository of SBOL 2-compliant biochemical parts for a wide range of metabolic engineering applications. The URL of our repository is http://www.cbrc.kaust.edu.sa/sbolme .

This talk describes the synthetic biology open language (SBOL).

The SynBioHub repository ( https://synbiohub.org ) is an open-source software project that facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g., the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.

Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs.

GNSS (Global Navigation Satellite System) data play a crucial role in both scientific research and practical applications. GNSS datasets are used to monitor atmospheric conditions, tectonic plate movements, and Earth deformation, providing valuable insights for geodetic and geophysical studies. Although widely accessible, GNSS data often lacks the necessary structure and metadata for effective reuse, particularly for data-driven research based on machine learning. To address these challenges, we applied the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to GNSS RINEX observation files hosted by the EUREF Historical Data Centre (EUREF-HDC).The EU action plan “Turning FAIR into Reality” introduced the concept of FAIR Digital Objects (FDOs), emphasizing the need for Persistent Identifiers (PIDs) and rich, standardized metadata to ensure data can be reliably found, accessed, utilized, and cited. Building on this foundation, we developed a multi-layered FDO structure centered on GNSS RINEX data. Given the established nature of the EUREF-HDC repository, we adapted the FDO concept by prioritizing structured metadata, followed by persistent identifiers and robust (meta)data access procedures.To implement this approach, we designed the GNSS-DCAT-AP metadata schema, assigned PIDs to both data and metadata, and developed web services enabling humans and machines alike to seamless search, retrieve, and download (meta)data. The effectiveness of our solution was evaluated using the FAIRsFAIR Data Object Assessment Metrics, demonstrating a significant improvement in FAIR compliance.This work showcases the feasibility of transforming GNSS RINEX data into FAIR Digital Objects and could provide a practical roadmap for other geospatial data repositories seeking alignment with FAIR principles.

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications.

Applying artificial intelligence (AI) and machine learning (ML) techniques to biomedical problems is predicated on access to clean, high-quality data and reusable software tools. Data and tool repositories offer a vital infrastructure for centralizing, storing, and sharing such resources with the global research community of cross-disciplinary scientists. The Cancer Complexity Knowledge Portal (CCKP) is a NIH-listed domain-specific repository that makes oncology data findable and accessible. The CCKP is developed and maintained by the Multi-Consortia Coordinating (MC2) Center, led by Sage Bionetworks. The MC2 Center supports resource coordination among six cancer-focused interdisciplinary research consortia funded by the National Cancer Institute (NCI). Through this work, the CCKP has emerged as a significant repository for oncology data and tool sharing. To define the metadata standards for sharing resources on the CCKP, we host data models for various data modalities including genomics and imaging, and are developing new models for emerging multimodal data types like spatial transcriptomics. The data models undergo iterative development based on emerging community needs, with versioned releases maintained in a public GitHub repository. These data models power established data management tools developed by Sage Bionetworks, including the Schematic Python package and Data Curator App, to support annotation of data according to FAIR standards. The data models are designed to help researchers link research outputs (e.g., publications, datasets), thus helping the CCKP synthesize and highlight the activities and outputs of the NCI-funded cancer research programs in a meaningful way. The CCKP provides search and filtering capability to accelerate discovery and collaboration in the cancer research community. As of November 2024, the portal hosts curated information on 3, 786 publications, 904 datasets, and 292 computational tools emerging from over 140 research grants. Our data models also incorporate elements of the Cancer Research Data Commons (CRDC) Data Hub data model to support integration and reuse of datasets within the CRDC ecosystem. The recent advances in computational oncology showcase the strength of secondary use of research data and highlight an unmet need to ensure that data and tools available through repositories are reliable, and reusable. We are engaging with scientists, clinicians, and patient advocates, and leveraging user-centered design approaches and structured data models to make cancer data and tools more findable, accessible, and reusable. These ongoing improvements of the portal aim to increase its usability, bringing experimental labs generating data and computational labs using data closer together, to fuel the iterative cycle of scientific discovery. Citation Format: Orion Banks, Ashley Clayton, Aditi Gopalan, Amber Nelson, Stockard Simon, Verena Chung, Amy Heiser, Jay Hodgson, Aditya Nath, Adam Hindman, Milen Nikolov, Adam Taylor, James Eddy, Susheel Varma, Jineta Banerjee. Cancer Complexity Knowledge Portal: a centralized web portal for finding cancer related data, software tools, and other resources [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1062.

Synthetic biology is an umbrella term that covers a range of aims, approaches, and techniques. They are all brought together by common practices of analogizing, synthesizing, mechanicizing, and kludging. With a focus on kludging as the connection point between biology, engineering, and evolution, I show how synthetic biology’s successes depend on custombuilt kludges and a creative, “make-it-work” attitude to the construction of biological systems. Such practices do not fit neatly, however, into synthetic biology’s celebration of rational design. Nor do they straightforwardly embody Richard Feynman’s “last blackboard” statement (1988) that without creating something it cannot be understood. Reflecting further on the relationship between synthetic construction and knowledge making gives philosophy of science new avenues of insight into scientific practice.

Our university intends to improve the central IT-support for management of research data. A core demand is supporting FAIR guiding principles. In order to make research data findable for future research projects, an application for the creation and storage of structured meta data for research data was developed. The created meta data repository enable creating, maintaining and querying research data based on discipline-specific properties. Since large number of meta data standards exist for different scientific domains, technologies from the areas of Linked Data and Semantic Web are used to process and store meta data. This work describes the requirements, the design and the implementation a the meta data application that can be integrated into existing research work flows and gives an overview of technical backgrounds used for creating the meta data repository.

The data set contains stream water concentrations of herbicides and nutrients for 153 sites in the northern Missouri/southern Iowa region from 1994 to 1995 and additional data from 1996 to 1999 for 21 sites. The data are available in Microsoft Excel 2010 format. Sheet 1 (Metadata) of the file contains supporting information regarding the length of record, site locations, parameters measured, concentrations units, method detection limits, describes the meaning of zero and blank cells, defines the major land resource areas (MLRAs) of the region, and provides a link to the U. S. Geological Survey discharge data. Sheet 2 (Site names and locations) has a list of the site names by MLRA, river system, and site name. It also contains site locations, provided as Universal Transverse Mercator coordinates, drainage areas, and indicates which sites were co-located at U. S. Geological Survey gauge sites. Sheet 3 (Concentration Data) contains data for 15 herbicide and nutrient analytes along with the corresponding site name, river system, and MLRA.