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数学模型在理解发育生物学中的模式形成方面的作用。

The role of mathematical models in understanding pattern formation in developmental biology.

作者信息

Umulis David M, Othmer Hans G

机构信息

Agricultural and Biological Engineering, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.

出版信息

Bull Math Biol. 2015 May;77(5):817-45. doi: 10.1007/s11538-014-0019-7. Epub 2014 Oct 4.

Abstract

In a Wall Street Journal article published on April 5, 2013, E. O. Wilson attempted to make the case that biologists do not really need to learn any mathematics-whenever they run into difficulty with numerical issues, they can find a technician (aka mathematician) to help them out of their difficulty. He formalizes this in Wilsons Principle No. 1: "It is far easier for scientists to acquire needed collaboration from mathematicians and statisticians than it is for mathematicians and statisticians to find scientists able to make use of their equations." This reflects a complete misunderstanding of the role of mathematics in all sciences throughout history. To Wilson, mathematics is mere number crunching, but as Galileo said long ago, "The laws of Nature are written in the language of mathematics[Formula: see text] the symbols are triangles, circles and other geometrical figures, without whose help it is impossible to comprehend a single word." Mathematics has moved beyond the geometry-based model of Galileo's time, and in a rebuttal to Wilson, E. Frenkel has pointed out the role of mathematics in synthesizing the general principles in science (Both point and counter-point are available in Wilson and Frenkel in Notices Am Math Soc 60(7):837-838, 2013). We will take this a step further and show how mathematics has been used to make new and experimentally verified discoveries in developmental biology and how mathematics is essential for understanding a problem that has puzzled experimentalists for decades-that of how organisms can scale in size. Mathematical analysis alone cannot "solve" these problems since the validation lies at the molecular level, but conversely, a growing number of questions in biology cannot be solved without mathematical analysis and modeling. Herein, we discuss a few examples of the productive intercourse between mathematics and biology.

摘要

在2013年4月5日发表于《华尔街日报》的一篇文章中,E. O. 威尔逊试图论证生物学家其实并不真的需要学习任何数学——每当他们在数值问题上遇到困难时,他们可以找一名技术人员(也就是数学家)来帮他们解决难题。他在威尔逊第一原则中将此观点形式化:“对科学家来说,从数学家和统计学家那里获得所需的合作要比数学家和统计学家找到能够运用他们方程的科学家容易得多。”这反映出对数学在整个历史上所有科学中所起作用的完全误解。对威尔逊而言,数学不过是数字运算,但正如伽利略很久以前所说:“自然法则是用数学语言写成的[公式:见正文]其符号是三角形、圆形和其他几何图形,没有这些图形的帮助,就不可能理解其中的任何一个字。”数学已经超越了伽利略时代基于几何的模式,在对威尔逊的反驳中,E. 弗伦克尔指出了数学在综合科学中的一般原理方面的作用(正反两方观点均可见于《美国数学会通告》2013年第60卷第7期第837 - 838页中威尔逊和弗伦克尔的文章)。我们将进一步探讨数学如何被用于在发育生物学中做出新的且经实验验证的发现,以及数学对于理解一个困扰实验学家数十年的问题——生物体如何在大小上进行缩放——是多么至关重要。仅靠数学分析无法“解决”这些问题,因为验证在于分子层面,但相反,生物学中越来越多的问题没有数学分析和建模就无法解决。在此,我们讨论一些数学与生物学之间富有成效的交流实例。

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