生物复杂性的历史本质和数学方法的无效

IF 1.3 4区生物学 Q3 BIOLOGY

Theory in Biosciences Pub Date : 2022-05-18 DOI:10.1007/s12064-022-00369-7

Saverio Forestiero

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引用次数: 2

摘要

当代科学知识是建立在方法论和认识论的还原论基础上的。在对某些物理现象进行数学处理时，发现了还原论范式的局限性，从而产生了复杂性的概念，既作为一种模式，也作为一种过程。在澄清了关于生物进化和生物复杂性的一些非常一般的术语和观点之后，本文将试图总结关于生物复杂性的争论，并讨论生物复杂性和惰性物质之间的区别。下面是一些应用于生物学问题的数学的主要成功的例子;将数学应用于生物复杂性作为个体和物种水平上的全局、关系和历史现象的内在限制的概念也将得到推进。

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The historical nature of biological complexity and the ineffectiveness of the mathematical approach to it

Contemporary scientific knowledge is built on both methodological and epistemological reductionism. The discovery of the limitations of the reductionist paradigm in the mathematical treatment of certain physical phenomena originated the notion of complexity, both as a pattern and process. After clarifying some very general terms and ideas on biological evolution and biological complexity, the article will tackle to seek to summarize the debate on biological complexity and discuss the difference between complexities of living and inert matter. Some examples of the major successes of mathematics applied to biological problems will follow; the notion of an intrinsic limitation in the application of mathematics to biological complexity as a global, relational, and historical phenomenon at the individual and species level will also be advanced.

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来源期刊

Theory in Biosciences 生物-生物学

CiteScore

2.70

自引率

9.10%

发文量

审稿时长

3 months

期刊介绍： Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.