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Leveraging eco-evolutionary models for gene drive risk assessment.

Matthew A Combs1, Andrew J Golnar1, Justin M Overcash2

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Engineered gene drives offer benefits but pose ecological risks. Dynamic modeling helps predict gene drive outcomes, guiding responsible development and risk assessment for these powerful genetic technologies.

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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.