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A common gene drive language eases regulatory process and eco-evolutionary extensions

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Verma,  Prateek
Research Group Theoretical Models of Eco-Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Reeves,  R. Guy
Research Group Population Genetics, Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Gokhale,  Chaitanya S.
Research Group Theoretical Models of Eco-Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Citation

Verma, P., Reeves, R. G., & Gokhale, C. S. (2021). A common gene drive language eases regulatory process and eco-evolutionary extensions. BMC Ecology and Evolution, 21: 156. doi:0.1186/s12862-021-01881-y.


Cite as: https://hdl.handle.net/21.11116/0000-0009-2E40-C
Abstract
Background Synthetic gene drive technologies aim to spread transgenic constructs into wild populations even when they impose organismal fitness disadvantages. The extraordinary diversity of plausible drive mechanisms and the range of selective parameters they may encounter makes it very difficult to convey their relative predicted properties, particularly where multiple approaches are combined. The sheer number of published manuscripts in this field, experimental and theoretical, the numerous techniques resulting in an explosion in the gene drive vocabulary hinder the regulators' point of view. We address this concern by defining a simplified parameter based language of synthetic drives. Results Employing the classical population dynamics approach, we show that different drive construct (replacement) mechanisms can be condensed and evaluated on an equal footing even where they incorporate multiple replacement drives approaches. Using a common language, it is then possible to compare various model properties, a task desired by regulators and policymakers. The generalization allows us to extend the study of the invasion dynamics of replacement drives analytically and, in a spatial setting, the resilience of the released drive constructs. The derived framework is available as a standalone tool. Conclusion Besides comparing available drive constructs, our tool is also useful for educational purpose. Users can also explore the evolutionary dynamics of future hypothetical combination drive scenarios. Thus, our results appraise the properties and robustness of drives and provide an intuitive and objective way for risk assessment, informing policies, and enhancing public engagement with proposed and future gene drive approaches.