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A probabilistic framework for identifying biosignatures using Pathway Complexity
One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configuration...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686400/ https://www.ncbi.nlm.nih.gov/pubmed/29133442 http://dx.doi.org/10.1098/rsta.2016.0342 |
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author | Marshall, Stuart M. Murray, Alastair R. G. Cronin, Leroy |
author_facet | Marshall, Stuart M. Murray, Alastair R. G. Cronin, Leroy |
author_sort | Marshall, Stuart M. |
collection | PubMed |
description | One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes—whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call ‘Pathway Complexity’, that allows us not only to threshold the abiotic–biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue ‘Reconceptualizing the origins of life’. |
format | Online Article Text |
id | pubmed-5686400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-56864002017-11-19 A probabilistic framework for identifying biosignatures using Pathway Complexity Marshall, Stuart M. Murray, Alastair R. G. Cronin, Leroy Philos Trans A Math Phys Eng Sci Articles One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes—whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call ‘Pathway Complexity’, that allows us not only to threshold the abiotic–biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue ‘Reconceptualizing the origins of life’. The Royal Society Publishing 2017-12-28 2017-11-13 /pmc/articles/PMC5686400/ /pubmed/29133442 http://dx.doi.org/10.1098/rsta.2016.0342 Text en © 2017 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Marshall, Stuart M. Murray, Alastair R. G. Cronin, Leroy A probabilistic framework for identifying biosignatures using Pathway Complexity |
title | A probabilistic framework for identifying biosignatures using Pathway Complexity |
title_full | A probabilistic framework for identifying biosignatures using Pathway Complexity |
title_fullStr | A probabilistic framework for identifying biosignatures using Pathway Complexity |
title_full_unstemmed | A probabilistic framework for identifying biosignatures using Pathway Complexity |
title_short | A probabilistic framework for identifying biosignatures using Pathway Complexity |
title_sort | probabilistic framework for identifying biosignatures using pathway complexity |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686400/ https://www.ncbi.nlm.nih.gov/pubmed/29133442 http://dx.doi.org/10.1098/rsta.2016.0342 |
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