Ce problème est capital pour l’astrobiologie, qu’il s’agisse de formes de vie passée ou présente. Mais sauf à pouvoir poser sur ces planètes des rovers dotés d’instruments lourds et possiblement inefficaces, comme en ce qui concerne aujourd’hui la Lune et Mars il ne peut recevoir de réponse.
Récemment cependant des scientifiques intéressés par la question, ont proposé dans le journal Proceedings of the National Academy of Sciences de faire appel à l’Intelligence Artificielle (IA). Leur méthode permettrait de distinguerait des exemplaires d’origine biologique anciens ou actuels de leurs équivalents abiotiques (non vivants). Les senseurs proposés pourraient à terme se livrer à des analyses physiques ou chimiques sur divers types d’échantillons avant leur retour sur terre..
Dans l’immédiat de telles techniques pourraient permettre analyser l’histoire d’anciennes roches présentes sur terre. Par ailleurs, l’un de ces instruments a déjà été nommé SAM car il étudiera des échantillons prélevés sur la planète Mars par le Rover Curiosity (Sample Analyse on Mars SAM.)
La recherche d’une éventuelle vie extraterrestre, qu’elle soit proche de la vie terrestre ou au contraire différente, représente aujourd’hui un enjeu scientifique et philosophique dont l’importance n’a pas besoin d’être soulignée. Que l’on pense aux milliers de milliards de galaxies actuellement découvertes par le James Ward Space Telescope/
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« We’ll need to tweak our method to match SAM’s protocols, but it’s possible that we already have data in hand to determine if there are molecules on Mars from an organic Martian biosphere. »
« The search for extraterrestrial life remains one of the most tantalizing endeavors in modern science, » says lead author Jim Cleaves of the Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC.
The implications of this new research are many, but there are three big takeaways: First, at some deep level, biochemistry differs from abiotic organic chemistry; second, we can look at Mars and ancient Earth samples to tell if they were once alive; and third, it is likely this new method could distinguish alternative biospheres from those of Earth, with significant implications for future astrobiology missions
Reference
https://www.pnas.org/doi/10.1073/pnas.2307149120
A robust, agnostic molecular biosignature based on machine learning
H. James Cleaves II https://orcid.org/0000-0003-4101-0654, Grethe Hystad https://orcid.org/0000-0001-9572-1019, Anirudh Prabhu https://orcid.org/0000-0002-9921-6084, +3, and Robert M. Hazen https://orcid.org/0000-0003-4163-8644 rhazen@carnegiescience.edu
Edited by Roger Summons, Massachusetts Institute of Technology, Cambridge, MA; received April 29, 2023; accepted July 17, 2023
September 25, 2023
120 (41) e2307149120
https://doi.org/10.1073/pnas.2307149120
Significance
We report a significant advance to one of the most important problems in astrobiology—the development of a simple, reliable, and practical method for determining the biogenicity of organic materials in planetary samples, both on other worlds and for the earliest traces of life on Earth. We have developed a robust method that combines pyrolysis GC-MS measurements of a wide variety of terrestrial and extraterrestrial carbonaceous materials with machine-learning-based classification to achieve ~90% accuracy in the differentiation between samples of abiotic origins vs. biotic specimens, including highly-degraded, ancient, biologically-derived samples. Such discrimination points to underlying “rules of biochemistry” that reflect the Darwinian imperative of biomolecular selection for function.
Abstract
The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis–gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine-learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine-learning methods do not rely on precise compound identification: Rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method’s utility for detecting alien biology.
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