Embracing Dynamic Models for Gene Drive Management

Andrew J Golnar; Emily Ruell; Alun L Lloyd; Kim M Pepin

doi:10.1016/j.tibtech.2020.08.011

Embracing Dynamic Models for Gene Drive Management

Trends Biotechnol. 2021 Mar;39(3):211-214. doi: 10.1016/j.tibtech.2020.08.011. Epub 2020 Sep 30.

Authors

Andrew J Golnar¹, Emily Ruell², Alun L Lloyd³, Kim M Pepin²

Affiliations

¹ National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, 4101 Laporte Ave., Fort Collins, CO 80521, USA. Electronic address: andrew.golnar@usda.gov.
² National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, 4101 Laporte Ave., Fort Collins, CO 80521, USA.
³ Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.

PMID: 33010965
DOI: 10.1016/j.tibtech.2020.08.011

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.

Keywords: GMO; decision-making; gene drive; population genetics; regulation; risk assessment; uncertainty.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Ecosystem*
Gene Drive Technology* / standards
Gene Drive Technology* / trends
Models, Genetic*
Risk Management