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): an extension of and 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 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 homing gene drive system intended to drive a disease-refractory gene into a population, incorporating time-varying temperature and rainfall data, and predict 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 an open-source R package freely available on CRAN. 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.

中文翻译：

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MGDrivE 2：包含季节性和流行病学动态的基因驱动系统模拟框架

随着实验室中开发出有希望的新驱动系统，对基因驱动技术的兴趣不断增长，并且讨论正在朝着实施现场试验的方向发展。野外试验的前景要求模型包含大量的生态细节，包括随温度和降雨等环境数据而变化的参数，从而导致蚊虫密度的季节性变化。流行病学结果也越来越重要，因为：i）基因驱动构建体是否适合释放将取决于其对疾病传播的预期影响，并且ii）初步的现场试验预计将具有可衡量的昆虫学结果和模型化的流行病学结果。我们展示了MGDrivE 2（蚊子基因驱动器浏览器2）：MGDrivE 1仿真框架的扩展和发展，它研究了各种基因驱动架构的种群动态及其在空间明确的蚊子种群中的传播。MGDrivE 2框架的主要优势和改进之处在于：i）参数随时间变化并引起季节性种群动态的能力； ii）适应人类和蚊子之间相互病原体传播的流行病学模块； iii）基于随机的实施框架Petri网可实现有效的模型制定和灵活的实施。进行了示例MGDrivE 2仿真，以演示该框架在基于CRISPR的归巢基因驱动系统中的应用，该系统旨在将疾病难治性基因驱动到种群中，结合随时间变化的温度和降雨数据，并预测对人类疾病发病率和患病率的影响。在项目的CRAN存储库的小插图中提供了更多的文档和使用示例。MGDrivE 2是CRAN上免费提供的开源R包。我们希望该软件包提供一个灵活的工具，能够随着基因驱动构建体向现场应用的发展而建模，并推断它们对疾病传播的预期影响。