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An applied mathematician's perspective on Rosennean complexity

Siekmann, I (2017) An applied mathematician's perspective on Rosennean complexity. Ecological Complexity. ISSN 1476-945X

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Abstract

The theoretical biologist Robert Rosen developed a highly original approach for investigating the question "What is life?", the most fundamental problem of biology. Considering that Rosen made extensive use of mathematics it might seem surprising that his ideas have only rarely been implemented in mathematical models. On the one hand, Rosen propagates relational models that neglect underlying structural details of the components and focus on relationships between the elements of a biological system, according to the motto "throw away the physics, keep the organisation". Rosen's strong rejection of mechanistic models that he implicitly associates with a strong form of reductionism might have deterred mathematical modellers from adopting his ideas for their own work. On the other hand Rosen's presentation of his modelling framework, (M, R) systems, is highly abstract which makes it hard to appreciate how this approach could be applied to concrete biological problems. In this article, both the mathematics as well as those aspects of Rosen's work are analysed that relate to his philosophical ideas. It is shown that Rosen's relational models are a particular type of mechanistic model with specific underlying assumptions rather than a fundamentally different approach that excludes mechanistic models. The strengths and weaknesses of relational models are investigated by comparison with current network biology literature. Finally, it is argued that Rosen's definition of life, "organisms are closed to efficient causation", should be considered as a hypothesis to be tested and ideas how this postulate could be implemented in mathematical models are presented. © 2017 Elsevier B.V.

Item Type: Article
Uncontrolled Keywords: 0501 Ecological Applications, 0602 Ecology
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics
Divisions: Applied Mathematics
Publisher: Elsevier
Date Deposited: 20 Sep 2017 11:08
Last Modified: 20 Sep 2017 11:08
DOI or Identification number: 10.1016/j.ecocom.2017.07.007
URI: http://researchonline.ljmu.ac.uk/id/eprint/7133

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