Leveraging eco-evolutionary models for gene drive risk assessment

Engineered gene drives create potential for both widespread benefits and irre-versible harms to ecosystems. CRISPR-based systems of allelic conversion have rapidly accelerated gene drive research across diverse taxa, putting field trials and their necessary risk assessments on the horizon. Dynamic process-based models provide flexible quantitative platforms to predict gene drive out-comes in the context of system-specific ecological and evolutionary features. Here, we synthesize gene drive dynamic modeling studies to highlight research trends, knowledge gaps, and emergent principles, organized around their ge-netic, demographic, spatial, environmental, and implementation features. We identify the phenomena that most significantly influence model predictions, dis-cuss limitations of biological complexity and uncertainty, and provide insights to promote responsible development and model-assisted risk assessment of gene drives.

Automated summary

Engineered gene drives have the potential to bring both widespread benefits and irreversible harms to ecosystems. CRISPR-based systems have greatly accelerated gene drive research, making field trials and risk assessments possible. Dynamic process-based models provide quantitative platforms to predict gene drive outcomes based on specific ecological and evolutionary features. This synthesis of gene drive dynamic modeling studies highlights research trends, knowledge gaps, and emergent principles, aiming to promote responsible development and risk assessment of gene drives.