PAPER: CRISPR modeling for invasive rodent control
URL: https://doi.org/10.1371/journal.pcbi.1009660
Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework
December 2021
Champer SE, Oakes N, Sharma R, Garcia-Diaz P, Champer J, Messer PW. 2021. Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework. PLoS Computational Biology 17(12): e1009660.
ABSTRACT
Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse, and could be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit, allows for overlapping generations and a fluctuating population size, and includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.
KEYWORDS
CRISPR gene drives; Invasive rodents; Supervised machine learning; Population model; Island populations; Resistance allele formation rate
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| License | CC-BY-4.0 |
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| State | active |