Scientists have developed a pioneering method based on artificial intelligence to detect signs of life on other planets. This method, with up to 90% accuracy, distinguishes between biological and non-biological samples by analyzing molecular patterns. It promises to revolutionize space exploration and our understanding of the origins of life, with potential applications in various fields including biology and archaeology.
“The Holy Grail of Astrobiology” – New Machine learning This technology can determine whether a sample is of biological or non-biological origin by 90%. Accuracy.
Scientists have discovered a simple, reliable test for signs of past or present life on other planets – the “Holy Grail of astrobiology.”
In a paper recently published in the journal Proceedings of the National Academy of SciencesIt is a seven-member team, funded by the John Templeton Foundation and led by Jim Cleaves and Robert Hazen of the John Templeton Foundation. Carnegie Institution for ScienceReports indicate that their AI-based method, with up to 90% accuracy, distinguished modern and ancient biological samples from those of non-biological origin.
A revolution in space exploration and Earth sciences
“This routine analytical method has the potential to revolutionize the search for extraterrestrial life and deepen our understanding of both the origin and chemistry of early life on Earth,” says Dr. Hazen. “It opens the way to using smart sensors on robotic spacecraft, landers and rovers to search for signs of life before samples return to Earth.”
More immediately, the new test could reveal the history of mysterious ancient rocks on Earth, and perhaps the history of samples already collected by scientists. Mars Curiosity’s Sample Analysis Instrument at Mars (SAM). The latter tests could be performed using an onboard analytical instrument called SAM (Sample Analysis on Mars).
This image taken by NASA’s Perseverance rover on August 6, 2021, shows the hole drilled in a Martian rock in preparation for the rover’s first attempt to collect a sample. This image was captured by one of the rover’s hazard cameras at what the rover’s science team called a “paving rock” in the “Crater Floor Fractured Rough” area of Jezero Crater. Image source: NASA/JPL-Caltech
“We will need to modify our method to suit SAM protocols, but it is possible that we already have data to determine whether there are molecules on Mars from the Martian organic biosphere.”
Key takeaways from the new research
“The search for extraterrestrial life remains one of the most exciting endeavors in modern science,” says lead author Jim Cleaves of the Earth and Planetary Laboratory at the Carnegie Institution for Science in Washington, D.C.
“The implications of this new research are many, but there are three main points to conclude: First, at a deep level, biochemistry is different from abiotic organic chemistry; Second, we can look at ancient Mars and Earth samples to see if they were alive in One day; third, this new method will likely be able to distinguish between alternative biospheres and those on Earth, with major implications for future astrobiology tasks.
The role of artificial intelligence in distinguishing between biological and non-biological samples
The innovative analytical method does not depend solely on identifying a specific molecule or group of compounds in the sample.
Instead, the researchers demonstrated that AI can distinguish between biological and abiotic samples by detecting subtle differences within a sample’s molecular patterns as revealed by pyrolysis gas chromatography (which separates and identifies the component parts of a sample), followed by mass spectrometry (which determines the weights molecular). of these components).
Big, multi-dimensional data from molecular analyzes of 134 samples rich in abiotic or biotic carbon were used to train artificial intelligence to predict the origin of the new sample. With approximately 90% accuracy, the AI successfully identified samples that originated from:
- Living organisms, such as modern shells, teeth, bones, insects, tree leaves, rice, human hair, and cells preserved in fine-grained rocks
- Remnants of ancient life that have been altered by geological processing (such as coal, oil, amber, and carbon-rich fossils), or
- Samples of non-biological origins, such as pure laboratory chemicals (e.g. Amino acids) and carbon-rich meteorites.
The authors add that until now it has been difficult to determine the origins of many ancient carbon-bearing samples, because collections of organic molecules, whether biotic or abiotic, tend to decompose over time.
Surprisingly, despite significant decay and change, the new analytical method has revealed biological markers preserved in some cases over hundreds of millions of years.
Deciphering the chemistry of life and the potential for future discoveries
“We started with the idea that the chemistry of life is fundamentally different from the chemistry of the inanimate world,” says Dr. Hazen. That there are “chemical rules of life” that influence the diversity and distribution of biomolecules. If we can deduce those rules, we can use them to guide our efforts to model the origins of life or detect subtle signs of life on other worlds.
“These results mean that we may be able to find a life form from another planet, or another biosphere, even if it is very different from the life we know on Earth. And if we find signs of life elsewhere, we can find out whether life on Earth and other planets exists.” Others derive from a common or different origin.
In other words, the method should be able to detect alien biochemistry, as well as life on Earth. This is important because it is relatively easy to discover molecular biomarkers of life on Earth, but we cannot assume that alien life would be used DNAAmino acids, etc. Our method looks for patterns in molecular distributions that arise from life’s need for “functional” molecules.
“What really amazed us was that we trained our machine learning model to predict only two types of specimens – biotic or abiotic – but the method detected three distinct groups: abiotic, biotic, and fossiliferous. In other words, it could identify newer biological specimens than fossiliferous specimens.” A fossil, for example, a freshly picked leaf or vegetable, versus something that died a long time ago. This surprising discovery gives us optimism that other features such as photosynthetic life or eukaryotes (cells with a nucleus) can be identified.
The analytical capabilities of artificial intelligence in detecting complex patterns
To explain the role of AI, co-author Anirudh Prabhu of the Carnegie Institution for Science uses the idea of separating coins using different attributes — monetary value, metal, year, weight, or radius, for example — and then goes further to find combinations Features that create more accurate separations and assemblies. “And when it comes to hundreds of these attributes, AI algorithms are invaluable for gathering information and creating highly accurate insights.”
“From a chemical point of view, the differences between biological and abiotic samples relate to things like water solubility, molecular weights, volatility, etc.,” Dr. Cleaves adds.
“The simple way I think about this is that the cell has a membrane and an interior called the cytosol; The membrane is somewhat insoluble in water, while the cell content is somewhat soluble in water. This arrangement maintains the membrane’s assembly while trying to minimize the contact of its components with water and also prevents “internal components” from leaking through the membrane.
“Intrinsic components can also remain water-soluble despite being very large molecules such as chromosomes and proteins,” he says.
“So, if one breaks a cell or living tissue down into its components, one gets a mixture of very water-soluble molecules and very water-insoluble molecules spread across a wide range. Things like petroleum and coal have lost most of their water-soluble material over Its long history.
“Biological samples can have unique distributions across this spectrum relative to each other, but also differ from biological distributions.”
3.5 billion year old Apex Chert from the wilds of Western Australia. Credit: Carnegie Laboratory for Earth and Planetary Sciences
This technology may soon solve a number of scientific mysteries on Earth, including the origin of 3.5 billion-year-old black deposits from Western Australia — highly controversial rocks that some researchers assert contain the oldest fossil microbes on Earth, while others claim It is devoid of life. Signs.
Other samples of ancient rocks in northern Canada, South Africa and China raise similar discussions.
“We are now applying our methods to answer these long-standing questions about the biogenesis of the organic matter found in these rocks,” Hazen says.
New ideas about the potential contributions of this new approach poured into other fields such as biology, paleontology, and archaeology.
“If artificial intelligence can easily distinguish between biotic and non-biotic life, as well as modern life from ancient life, what other insights might we gain? For example, could we know whether an ancient fossil cell had a nucleus, or was performing a process Photosynthesis? says Dr. Hazen.
“Is it possible to analyze charred remains and distinguish different types of wood from an archaeological site? It is as if we are dipping our toes in the water of a vast ocean of possibilities.”
Reference: “Robust, non-specific machine learning-based molecular biosignature” by H. James Cleaves, Jericht Hystad, Anirudh Prabhu, and Michael L. Wong, and George D. Cody, Sophia Economon, and Robert M. Hazen, September 25, 2023, Proceedings of the National Academy of Sciences.
doi: 10.1073/pnas.2307149120
The study was funded by the John Templeton Foundation.
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