{"doi":"10.1371/journal.pcbi.1009660","title":"Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework","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.","journal":"PLoS Computational Biology","year":2021,"id":162270,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":49,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9536,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":576888,"name":"Nathan Oakes","orcid":"0000-0002-5571-6036","position":1,"is_corresponding":false},{"id":576889,"name":"Ronin Sharma","orcid":"0000-0002-8384-0325","position":2,"is_corresponding":false},{"id":576890,"name":"Pablo García‐Díaz","orcid":"0000-0001-5402-0611","position":3,"is_corresponding":false},{"id":256310,"name":"Jackson Champer","orcid":"0000-0002-3814-3774","position":4,"is_corresponding":false},{"id":237910,"name":"Philipp W. Messer","orcid":"0000-0001-8453-9377","position":5,"is_corresponding":false},{"id":267758,"name":"Samuel E. Champer","orcid":"0000-0002-4559-7627","position":0,"is_corresponding":true}],"reference_count":97,"raw_metadata":null,"created_at":"2026-07-18T23:45:00.600065Z","pmid":"34965253","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}