Leveraging eco-evolutionary models for gene drive risk assessment

Area of Science:

• Ecology
• Genetics
• Computational Biology

Background:

• Engineered gene drives, particularly CRISPR-based systems, are advancing rapidly across species.
• The potential for widespread ecological impacts necessitates thorough risk assessment for upcoming field trials.

Purpose of the Study:

• To synthesize dynamic modeling studies of gene drives.
• To identify trends, knowledge gaps, and principles in gene drive modeling.
• To inform responsible development and risk assessment of gene drive technologies.

Main Methods:

• Systematic review and synthesis of dynamic process-based gene drive modeling studies.
• Analysis organized by genetic, demographic, spatial, environmental, and implementation features.
• Identification of key phenomena influencing model predictions and sources of uncertainty.

Main Results:

• Gene drive modeling research shows diverse trends and highlights critical knowledge gaps.
• Specific genetic, demographic, spatial, and environmental factors significantly impact predicted gene drive dynamics.
• Model limitations include biological complexity and inherent uncertainties.

Conclusions:

• Dynamic modeling is crucial for predicting gene drive behavior and assessing ecological risks.
• Understanding model influences and limitations is key to responsible gene drive deployment.
• Further research should focus on refining models to incorporate greater biological realism and uncertainty.