From the line: laura thomas
Newswise – The dangers posed by using data-centric methods in engineered biology have been identified by experts at the University of Bristol in a bid to make future research safer.
The potential misuse of data-centric approaches in synthetic biology poses significant risks. Ease of access to data science tools may enable nefarious actors to develop harmful biological agents for purposes such as bioterrorism or to deliberately disrupt ecological systems.
The findings, published today synthetic biology, Suggest additional data risk labels that describe data-related risks in the field of synthetic biology.
- Uncertain accuracy of source data – The accuracy of the underlying data is not known and therefore its use may lead to inaccurate results or bias.
- Uncertain completeness of source data – The underlying data is of uncertain completeness and contains missing values which lead to biased results.
- integration of inconsistent data – Data of different types and/or sources being used simultaneously may not be compatible with each other.
- capable of causing ecological damage – This technology has the potential to cause widespread ecological damage even when used properly.
- potential experimental hazard – Safety precautions may be required to translate the technology into experimental practice.
The work is the result of a collaboration between researchers at the Bristol Center for Engineering Biology (BrisEngBio) and. Gene Golding Institute for Data Intensive Research,
Kiran SharmaCo-author and PhD student working in AI for cellular modeling in the School of Engineering Mathematics and Technology said: “We are entering a transformative era where artificial intelligence and synthetic biology unite to revolutionize biological engineering. are taking place, accelerating the discovery of novel compounds.” From life-saving pharmaceuticals to sustainable biofuels.
“Our study highlights the potential risks associated with the specific types of data being used to train the latest systems biology models. For example, inconsistencies in measurements of complex and dynamic living organisms and privacy concerns that may compromise the security of next-generation models trained on human genome data.
This project extends the work of the Data Threats Project (datahazards.com), which aims to create a clear glossary of potential risks to data science research.
Co-author and co-lead of the Data Threats Project, Dr Nina De Cara from the School of Psychological Science, explained: “Having a clear vocabulary of risks makes it easier for researchers to think proactively about what the risks of their work are and take steps that reduce them. This makes communication easier for people working in different fields who sometimes use different language to talk about similar issues.
To achieve these clear terminologies, interdisciplinary collaboration is necessary.
Dr Daniel LawsonJean Golding Institute Director and Associate Professor in Data Science in the School of Mathematics said: “As datasets grow in magnitude and ambition, increasingly sophisticated algorithms are developed to gain new insights. This complexity necessitates a seamless collaborative approach to identifying and preventing downstream harm.
Dr. Thomas GorochowskiSenior author and Associate Professor of Biological Engineering in the School of Biological Sciences said: “Data science is set to revolutionize how we approach global challenges ranging from materials and fuel development to sustainable production of materials and fuels.” Let us engineer biology to harness its unique capabilities.” Innovative therapeutics. The extensions developed by our team will help bioengineers consider and discuss the risks around a data-centric approach to their research and help ensure that the vast benefits of bio-based solutions are realized in a safe manner.
The study was funded and supported by the Royal Society, BBSRC and EPSRC Bristol Biodesign Institute,
paper:
‘Data Threats in Synthetic Biology’ by Natalie R Zelenka, Nina Di Cara, Kiren Sharma, Thomas E Gorochowski et al. synthetic biology,
(TagstoTranslate)NewsWise(T)Synthetic Biology;Data(T)All Journal News(T)Engineering(T)Top Hit Stories(T)University of Bristol
