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Using statistical methods to model the fine-tuning of molecular machines and systems
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 22020 (English)In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 501, article id 110352Article in journal (Refereed) Published
Abstract [en]

Fine-tuning has received much attention in physics, and it states that the fundamental constants of physics are finely tuned to precise values for a rich chemistry and life permittance. It has not yet been applied in a broad manner to molecular biology. However, in this paper we argue that biological systems present fine-tuning at different levels, e.g. functional proteins, complex biochemical machines in living cells, and cellular networks. This paper describes molecular fine-tuning, how it can be used in biology, and how it challenges conventional Darwinian thinking. We also discuss the statistical methods underpinning fine-tuning and present a framework for such analysis.

Place, publisher, year, edition, pages
2020. Vol. 501, article id 110352
Keywords [en]
Bayesian, Fine-tuning, Complexity, Specificity, Intelligent Design, Waiting time problem, Model selection
National Category
Biological Sciences Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-184334DOI: 10.1016/j.jtbi.2020.110352ISI: 000551665300011PubMedID: 32505827OAI: oai:DiVA.org:su-184334DiVA, id: diva2:1471960
Available from: 2020-09-30 Created: 2020-09-30 Last updated: 2022-02-25Bibliographically approved

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Hössjer, Ola

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