Bayesian network-based risk assessment of synthetic biology: Simulating CRISPR-Cas9 gene drive dynamics in invasive rodent management

Assessing potential hybridization between a hypothetical gene drive-modified Drosophila suzukii and nontarget Drosophila species

Genetically engineered gene drives (geGD) are potentially powerful tools for suppressing or even eradicating populations of pest insects. Before living geGD insects can be released into the environment, they must pass an environmental risk assessment to ensure that their release will not cause unacceptable harm to non-targeted entities of the environment. A key research question concerns the likelihood that nontarget species will acquire the functional GD elements; such acquisition could lead to reduced abundance or loss of those species and to a disruption of the ecosystem services they provide. The main route for gene flow is through hybridization between the geGD insect strain and closely related species that co-occur in the area of release and its expected dispersal. Using the invasive spotted-wing drosophila, Drosophila suzukii, as a case study, we provide a generally applicable strategy on how a combination of interspecific hybridization experiments, behavioral observations, and molecular genetic analyses can be used to assess the potential for hybridization.

Leveraging eco-evolutionary models for gene drive risk assessment

Engineered gene drives create potential for both widespread benefits and irreversible 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 outcomes 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 genetic, demographic, spatial, environmental, and implementation features. We identify the phenomena that most significantly influence model predictions, discuss limitations of biological complexity and uncertainty, and provide insights to promote responsible development and model-assisted risk assessment of gene drives.