从缩小主义到现实主义:对复杂的生物系统进行整体数学建模
Ramón Nartallo-Kaluarachchi1,2, Renaud Lambiotte1, Alain Goriely1
1Mathematical Institute, University of Oxford, Oxford, UK.
Journal of the Royal Society, Interface
|November 26, 2025
概括
生物系统的复杂性太大,不能以简化主义的方法来解决. 需要一个整体的数学建模范式,使用先进的数据和计算,以便在数学生物学中获得可操作的见解.
科学领域:
- 数学生物学的数学生物学
- 计算神经科学是一种计算神经科学.
背景情况:
- 物理学的缩小主义方法在简单的系统中表现出色,但与生物复杂性作斗争.
- 生物系统表现出异质性,多功能性和多层次的相互作用.
- 传统的复杂系统建模缺乏预测性,经验基础的生物学见解.
研究的目的:
- 为生物学提出一个新的数学建模范式.
- 解决缩小主义和传统复杂系统方法的局限性.
- 为生物建模利用高分辨率数据和高性能计算.
主要方法:
- 采用一个整体的数学建模范式.
- 使用丰富的表示结构,如注释和多层网络.
- 采用基于代理的模型和基于模拟的方法.
- 专注于从观测中推断系统动态 (反向问题).
主要成果:
- 证明了在生物科学中超越缩小主义的必要性.
- 突出了神经科学和其他生物领域整体建模的潜力.
- 显示与寻找基本生物物理原理的相容性.
结论:
- 转向整体的,包容复杂性的数学建模对于推动生物科学的发展至关重要.
- 这种整合数据和计算的方法将推动数学生物学的进步.
- 神经科学作为这一范式转变的关键案例研究.
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