Although synthetic biology is now an established field, ethical reflection surrounding it has only recently become a subject of sustained scientific inquiry. This poster displays the results from a series of network analyses examining how synthetic biology ethics is evolving. The studies gather ethics articles from 802 institutions and 2,179 authors and suggest that the fields’ social structure has become democratized at the individual level but remains dominated by a handful of institutions at the organizational level.
Synthetic biology has transformative potential in a variety of application areas including agriculture, energy, materials, and health. While much of the research in this field has been in E. Coli, many applications require yeast and other bacteria. Researchers often use trial-and-error, since information can be difficult to locate. The goal of the Synthetic Biology Knowledge System (SBKS) is to create an an open and integrated resource that harnesses disparate, heterogeneous data sources to accelerate scientific exploration and discovery. This abstract gives an overview of the SBKS project, while several other abstracts submitted to this workshop explain different aspects in more detail.
The Synthetic Biology Knowledge System (SBKS) is a repository system designed to aid synthetic biologists by interlinking the synthetic biology literature through the application of ontologies. While many of these linking tasks are concerned with DNA/protein sequences, the organisms they are cultivated in, or chemical products of cellular processes, it is equally important to interlink the various ethical issues that concern the tailoring biological life in the form of bacteria and yeasts for participation in industrial processes to consider. Thus, an additional linking task for the SBKS is to link pertinent bioethics articles to biological entities, processes, and products that are central to the research of synthetic biologists. In this paper, we make a preliminary analysis of the state of the ethical discourse in the burgeoning synthetic biology discipline.
With the massive growth of community designed parts, opensource repositories such as SynBioHub  have become increasingly popular among synthetic biologists as a convenient way to store and share their genetic designs online. However, the sheer size of such repositories make it difficult to simply browse for the desired parts. The reference instance https://synbiohub.org) contains over 100,000 publicly available parts in its repository, not counting the various private repositories added by users. Currently, users can only find a part based on a keyword in the part’s description, or through various filters such as date of creation, creator, or collection. However, prior to this work, it was not possible in SynBioHub to search for similar sequences.
Synthetic biologists often use diagrams to visualize the structure and functionality of genetic designs due to their complicated nature. The Synthetic Biology Open Language Visual (SBOLv) is a standard for these diagrams. This standard provides a set of glyphs for synthetic biology components and how they can interact. These visual designs also have a complementary data standard, the Synthetic Biology Open Language (SBOL), which represents the structural and functional information for genetic designs This paper describes SBOLCanvas, an updated web-based genetic design editor that can create visual diagrams using all features of SBOLv2.
This paper describes our work using named entity recognition (NER), a sub-field of text mining, to mine existing literature. The goal of NER is to locate and classify named entities present in text into pre-defined categories. For synthetic biology, examples of such categories are names of genes, vectors, and regulatory elements. NER in biology domains has additional challenges due to the pace of new named entities being added, lack of naming convention, lengthy names, presence of special characters, and frequent and variable use of abbreviations.
Synthetic biology is a movement to standardize genetic engineering and make it more repeatable. An important advancement was the development of standardized genetic parts known as BioBricks, which can be composed using restriction enzyme assembly. The iGEM (international Genetically Engineered Machines) competition is an important synthetic biology outreach activity which is run by the iGEM foundation in keeping with their principles of the advancement of synthetic biology via education, competition, and development of an open and collaborative community. As part of the iGEM competition students submit records of any ‘parts’ they create to the iGEM registry (http://parts.igem.org/Main_Page). The iGEM registry was converted to the Synthetic Biology Open Language (SBOL) data format, a standard language for describing genetic designs, and a preliminary analysis of the data was carried out to predict the size of a potential library as well as quantify current problems with the registry data set.
This talk will discuss early application of linear regression models to optimize combinatorial libraries of biosynthetic pathways and how this frames the future of AI in synthetic biology. Through a discussion of several biological systems constructed in yeast, we will show how heterologous pathway engineering lends itself to machine learning to predict desired enzyme expression levels for optimal function. We will then show how we are working to include host modifications within this framework. In the course of expanding the number of variables in algorithmic optimization workflows, we have begun to encounter limits in biological quantitation. Current methods for obtaining biological information are practically datapoor for machine learning and much of the information is aggregated by experts over the corpus of literature. Therefore, there is motivation to construct searchable databases and conduct literature mining to assemble all the information necessary to initiate and modify a design. Thus, we argue that supporting the genetic designer is a grand challenge facing those who wish to continue to find new applications for AI in synthetic biology.