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Catch Me If You Can: A Spatial Model for a Brake-Driven Gene Drive Reversal

  • Léo GirardinEmail author
  • Vincent Calvez
  • Florence Débarre
Original Article

Abstract

Population management using artificial gene drives (alleles biasing inheritance, increasing their own transmission to offspring) is becoming a realistic possibility with the development of CRISPR-Cas genetic engineering. A gene drive may, however, have to be stopped. “Antidotes” (brakes) have been suggested, but have been so far only studied in well-mixed populations. Here, we consider a reaction–diffusion system modeling the release of a gene drive (of fitness \(1-a\)) and a brake (fitness \(1-b\), \(b\le a\)) in a wild-type population (fitness 1). We prove that whenever the drive fitness is at most 1/2 while the brake fitness is close to 1, coextinction of the brake and the drive occurs in the long run. On the contrary, if the drive fitness is greater than 1/2, then coextinction is impossible: the drive and the brake keep spreading spatially, leaving in the invasion wake a complicated spatiotemporally heterogeneous genetic pattern. Based on numerical experiments, we argue in favor of a global coextinction conjecture provided the drive fitness is at most 1/2, irrespective of the brake fitness. The proof relies upon the study of a related predator–prey system with strong Allee effect on the prey. Our results indicate that some drives may be unstoppable and that if gene drives are ever deployed in nature, threshold drives, that only spread if introduced in high enough frequencies, should be preferred.

Keywords

Long-time behavior Gene drive Brake Predator–prey Strong Allee effect 

Mathematics Subject Classification

35K57 37N25 92D10 92D25 

Notes

Acknowledgements

The authors thank three anonymous referees for valuable comments which lead to an improvement of the manuscript. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 639638). This work was supported by a public grant as part of the Investissement d’avenir Project, Reference ANR-11-LABX-0056-LMH, LabEx LMH, and ANR-14-ACHN-0003-01.

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Copyright information

© Society for Mathematical Biology 2019

Authors and Affiliations

  1. 1.Laboratoire de Mathématiques d’Orsay, Université Paris Sud, CNRSUniversité Paris-SaclayOrsay CedexFrance
  2. 2.Institut Camille Jordan, UMR 5208 CNRSUniversité Claude Bernard Lyon 1VilleurbanneFrance
  3. 3.CNRS, Sorbonne Université, Université Paris Est Créteil, Université Paris Diderot, INRA, IRD, Institute of Ecology and Environmental Sciences - ParisIEES-ParisParisFrance

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