SBOL-OWL: An ontological approach for formal and semantic representation of synthetic biology information

Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies

A genome wide analysis of the response to uncapped telomeres in budding yeast reveals a novel role for the NAD+ biosynthetic gene BNA2 in chromosome end protection

NAD+ metabolism may be linked to telomere end protection in yeast

The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs.

Recently, synthetic biologists have developed the Synthetic Biology Open Language (SBOL), a data exchange standard for descriptions of genetic parts, devices, modules, and systems. The goals of this standard are to allow scientists to exchange designs of biological parts and systems, to facilitate the storage of genetic designs in repositories, and to facilitate the description of genetic designs in publications. In order to achieve these goals, the development of an infrastructure to store, retrieve, and exchange SBOL data is necessary. To address this problem, we have developed the SBOL Stack, a Resource Description Framework (RDF) database specifically designed for the storage, integration, and publication of SBOL data. This database allows users to define a library of synthetic parts and designs as a service, to share SBOL data with collaborators, and to store designs of biological systems locally. The database also allows external data sources to be integrated by mapping them to the SBOL data model. The SBOL Stack includes two Web interfaces: the SBOL Stack API and SynBioHub. While the former is designed for developers, the latter allows users to upload new SBOL biological designs, download SBOL documents, search by keyword, and visualize SBOL data. Since the SBOL Stack is based on semantic Web technology, the inherent distributed querying functionality of RDF databases can be used to allow different SBOL stack databases to be queried simultaneously, and therefore, data can be shared between different institutes, centers, or other users

Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs

Feedback between p21 and reactive oxygen production is necessary for cell senescence

The sustained activation of CDKN1A (p21/Waf1/Cip1) by a DNA damage response induces mitochondrial dysfunction and reactive oxygen species (ROS) production via signalling through CDKN1A-GADD45A-MAPK14- GRB2-TGFBR2-TGFbeta in senescing primary human and mouse cells in vitro and in vivo.Enhanced ROS production in senescing cells generates additional DNA damage. Although this damage is repairable and transient, it elevates the average levels of DNA damage response permanently, thus forming a positive feedback loop.This loop is necessary and sufficient to maintain the stability of growth arrest until a ‘point of no return' is reached during establishment of senescence

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization

Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general

Toward full-stack in silico synthetic biology: integrating model specification, simulation, verification, and biological compilation

YesWe present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high- performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modelling, simulation, verification and bicompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modelling or specification languages, IBL uniquely blends modelling, verification and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.The work of S.K. is supported by EPSRC (EP/R043787/1). N.K., A.W., and B.B. acknowledge a Royal Academy of Engineering Chair in Emerging Technologies award and an EPSRC programme grant (EP/N031962/1)

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol.Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the Computational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation