打开潘多拉的盒子:在生物学中的数学建模中使用基于工具箱的方法的警告
1Host-Pathogen Interactions program, Texas Biomedical Research Institute, San Antonio, TX, USA.
Frontiers in applied mathematics and statistics
|February 5, 2025
概括
生物学中的数学建模提供了洞察力,但由于过度依赖自动化工具而受到阻碍. 真正的理解需要将这些方法与直观的见解平衡起来,认识到建模是一种艺术,而不仅仅是工程.
科学领域:
- 数学生物学 数学生物学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 数学建模对于理解生物系统至关重要,涉及分析和数值分析.
- 该领域的复杂性不断增长,导致新的分析和数据比较工具.
- 这些工具包括敏感性分析,信息标准 (AIC/BIC) 和混合效应建模.
研究的目的:
- 批判性地评估"工具箱"方法对生物学数学建模的影响.
- 认为这些自动化方法可以掩盖对模型行为的基本理解.
- 倡导在数学生物学中恢复直观见解.
主要方法:
- 对建模原理的哲学分析.
- 以工具箱为基础的方法存在局限性的说明性例子.
- 讨论自动化与洞察力生成之间的平衡.
主要成果:
- 过度依赖自动化分析工具可能会对理解生物模型的核心目标产生负面影响.
- 工具箱方法可能会阻碍对系统动态产生深入洞察力.
- 当前的方法可以导致模型技术的机械应用.
结论:
- 在生物学中的数学建模应该优先考虑与自动化分析一起的直观理解.
- 需要保持平衡,以确保"工具箱"方法增强而不是取代基本见解.
- 承认数学建模是一种艺术,是产生有意义的生物发现的关键.
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