Table 2.

Descriptions of additional data hazards relevant for synthetic biology

Data hazard	Description	Synthetic biology examples	Potential safeguards
Uncertain accuracy of source data	The accuracy of the underlying data is not known and so its use may lead to erroneous results or introduce bias.	Metabolic modeling where inaccurately labeled conversions (e.g. due to computational prediction) might lead to unexpected products being produced by engineered pathways.	Attempt to classify uncertainty if possible to better inform decisions and understand the range of possible outcomes.
Uncertain completeness of source data	Underlying data are of an uncertain completeness and have missing values that causes biased results.	Whole-cell models which attempt to use all the data available, but which may be limited. Protein design often builds on sequences on those proteins so far seen, which may bias design software.	Enrich data sets with missing data or attempt to correct for known biases.
Integration of incompatible data	Data of different types and/or sources are being used together that may not be compatible with each other.	Models that need to integrate information about many different processes in a cell.	Convert data to compatible format where possible of collect complementary data that is compatible.
Capable of ecological harm	This technology has the potential to cause broad ecological harm, even if used correctly.	Gene drives used to cause extinction events and in situ engineering of microbiomes.	Ensure sufficient physical containment to avoid unexpected release and barriers in place if deployed.
Potential experimental hazard	Translating technology into experimental practice can require safety precautions	Toxin production, virus-like particles, work with potentially pathogenic microbes.	Assess possible safety issues and put in place necessary safety measures.