Embracing Dynamic Models for Gene Drive Management

Authors

Andrew J. Golnar, NWRC, USDA APHISFollow
Emily W. Ruell, USDA, APHIS, WS, National Wildlife Research CenterFollow
Alun L. Lloyd, North Carolina State UniversityFollow
Kim M. Pepin, USA National Wildlife Research Center, USDA-APHISFollow

ORCID IDs

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

2021

Citation

Trends in Biotechnology, March 2021, Vol. 39, No. 3, pp 211-214

doi:10.1016/j.tibtech.2020.08.011

Comments

U.S. gov't work

Abstract

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) [1]. 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].