Modularity in biological thought: Sketch of a unifying theoretical framework

IF 2 4区 生物学 Q2 BIOLOGY
Luca Rivelli
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引用次数: 0

Abstract

This paper sketches a theoretical conception of modularity by generalizing Herbert Simon's idea of near-decomposability, showing that it could constitute a framework for the unification of the notion of modularity in the history and philosophy of biology. To put the framework to test, first a main problematic area is highlighted--the evolution of modularity--focusing on a historical and rational reconstruction of two ways of viewing it that appeared in the second half of the 20th century: one, due to Herbert Simon, rooted in a generic Darwinian mindset, the other, by Stuart Kauffman, inspired by a systemic-oriented approach tending to demote the importance of natural selection. It is shown that, under the light of the general view of modularity proposed here, these two apparently incompatible views can be interpreted as fundamentally homologous. The paper then engages with some current prominent views on modularity in biology, in order to show that the proposed framework is largely compatible with them, and able to accommodate cases of emergent modularity. The facilitating role of modularity in mechanistic and functional explanations is also highlighted. As a conclusion, it seems the proposed sketch of a theoretical view of modularity, open to further improvement, already shows potentiality as a unifying framework for the notion of modularity in philosophy and history of biology, and possibly, other disciplines.
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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