Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America.
Biophysics Graduate Group, Division of Biological Sciences, College of Letters and Science, University of California, Berkeley, California, United States of America.
PLoS Comput Biol. 2021 May 21;17(5):e1009030. doi: 10.1371/journal.pcbi.1009030. eCollection 2021 May.
Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.
人们对基因驱动技术的兴趣持续增长，因为实验室中已经开发出了有前途的新型驱动系统，并且正在讨论实施现场试验。现场试验的前景需要包含高度生态细节的模型，包括随时间变化的参数，以响应环境数据（如温度和降雨量），从而导致蚊子种群密度的季节性模式。流行病学结果也变得越来越重要，原因有二：i）基因驱动结构的适用性取决于其对疾病传播的预期影响，ii）初步现场试验预计会产生可衡量的昆虫学结果和模拟的流行病学结果。我们提出了 MGDrivE 2（蚊子基因驱动探索者 2）：这是 MGDrivE 1 模拟框架的重大发展，该框架调查了各种基因驱动结构的种群动态及其在空间明确的蚊子种群中的传播。MGDrivE 2 框架的主要优势和基本改进包括：i）参数随时间变化并引起季节性种群动态的能力，ii）容纳人类和蚊子之间相互传播病原体的流行病学模块，以及 iii）基于随机 Petri 网的实现框架，可实现高效的模型公式化和灵活的实现。示例 MGDrivE 2 模拟展示了该框架在用于以可控制和可逆方式将疾病抗性基因驱动到种群中的基于 CRISPR 的分裂基因驱动系统中的应用，该系统结合了随时间变化的温度和降雨数据。这些模拟还评估了对人类疾病发病率和流行率的影响。进一步的文档和使用示例在项目的 CRAN 存储库中的简介中提供。MGDrivE 2 可作为开源 R 包在 CRAN 上免费获得（https://CRAN.R-project.org/package=MGDrivE2）。我们希望该软件包能够提供一种灵活的工具，能够对基因驱动结构进行建模，因为它们更接近现场应用，并推断它们对疾病传播的预期影响。
