Andrew J. Golnar https://orcid.org/0000-0003-0747-5271
Emily Ruell https://orcid.org/0000-0002-9990-264X
Alun L. Lloyd https://orcid.org/0000-0002-6389-6321
Kim M. Pepin https://orcid.org/0000-0002-9931-8312
Date of this Version
Trends in Biotechnology, March 2021, Vol. 39, No. 3, pp 211-214
Robust methods of predicting how gene drive systems will interact with ecosystems is essential for safe deployment of gene drive technology. We describe how quantitative tools can reduce risk uncertainty, streamline empirical research, guide risk management, and promote cross-sector collaboration throughout the process of gene drive technology development and implementation.
Gene drive technologies, although diverse in design and mode of action, are molecular architectures that promote the transmission of genetic information between generations. In theory, the release of one gene-drive-modified organism (GDMO) has the potential to irreversibly alter species, ecosystems, and environmental processes at a global scale (although in practice numerous mechanisms can limit invasiveness) . This alarming and tremendous potential is an unprecedented challenge to biotechnology management that demands a different scope of oversight and coordination between public stakeholders, developers, and regulators [2,3]. Responsible management of GDMOs needs robust methods of risk assessment that account for and reduce uncertainties across different geographic and ecological contexts [1–3].
Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Other Environmental Sciences Commons, Other Veterinary Medicine Commons, Population Biology Commons, Terrestrial and Aquatic Ecology Commons, Veterinary Infectious Diseases Commons, Veterinary Microbiology and Immunobiology Commons, Veterinary Preventive Medicine, Epidemiology, and Public Health Commons, Zoology Commons