对经历迁移和扩散的生物种群的随机模型的推断和预测
Matthew J Simpson1, Michael J Plank2
1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Journal of the Royal Society, Interface
|October 28, 2025
概括
本综述探讨了基于随机代理的模型的参数推理,这对于理解生物变异性至关重要. 它强调了模型预测的方法,并提供了在生态和细胞动态中可重复性的开源代码.
科学领域:
- 计算生物学 计算生物学
- 数学生物学 数学生物学
- 系统生物学 系统生物学
背景情况:
- 参数推断对于用数学模型解释生物数据至关重要,尤其是在处理稀疏和杂的测量时.
- 确定性模型已经很成熟,但生物系统往往需要随机方法来解释固有的变化和随机性.
- 随机步行模型对于时空生物过程,如种群动态和分子运输特别有价值.
研究的目的:
- 审查与生物学相关的基于随机代理的模型的参数推断,可识别性分析和模型预测.
- 专注于生物启发的模型,适用于动物分散和细胞群.
- 强调数值优化和自动差异化在模型预测中的作用.
主要方法:
- 专注于基于随机代理的模型,包括随机步行模型.
- 应用参数推断和可识别性分析技术.
- 利用数值优化和自动差异化用于模型预测.
主要成果:
- 提供了一个全面的概述的参数推理在生物背景下基于随机代理的模型.
- 展示了数值优化和自动差异化的实用性,以提高模型预测能力.
- 提供开源的 Julia 代码,以促进科学可复制性和应用.
结论:
- 基于随机代理的模型对于捕捉生物复杂性和变异性至关重要.
- 像自动区分这样的先进计算方法显著提高了模型预测的准确性.
- 提供的开源代码使研究人员能够将这些方法应用于各种生物应用,并适应这些方法.
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