New AI System Could Revolutionize Search For Extraterrestrial Life

A new AI system that can determine extraterrestrial life with 90 percent accuracy could find signs of life on Mars say scientists.

The researchers have hailed the reliable new method as the ‘holy grail’ of astrobiology which can detect life on any planet or moon.

The American team developed a technique that can distinguish both modern and ancient biological samples from those not derived from living organisms.

They excitedly say it has the potential to revolutionize humanity’s search for extraterrestrial life and help us to understand the origins of the earliest lifeforms on Earth.

A seven-member team of American researchers demonstrated that artificial intelligence (AI) can differentiate between organic samples that contain or once contained life and abiotic samples that do not and never have contained life.

The AI detects subtle differences within the samples’ molecular patterns revealed by pyrolysis gas chromatography analysis – which separates and identifies a sample’s component parts – followed by mass spectrometry, which determines the molecular weights of those components.

The researchers used multidimensional data from the molecular analyses of 134 known abiotic or biotic carbon-rich samples to train the AI to predict a new sample’s origin with an accuracy of around 90 percent.

The AI successfully identified samples that originated from living things such as shells, teeth, bones, insects, leaves, rice, human hair, and cells preserved in fine-grained rock, as well as remnants of ancient life altered by geological processing like coal, oil, amber, and carbon-rich fossils.

It could equally identify samples with abiotic origins such as pure laboratory chemicals like amino acids and carbon-rich meteorites.

Until now, the origins of many ancient carbon-bearing samples have been difficult to place because collections of organic molecules – biotic or abiotic – tend to degrade over time.

But in spite of significant decay and alteration, the new analytical method was surprisingly able to detect signs of biology preserved in some instances over hundreds of millions of years.

“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 the earliest life on Earth,” Dr. Robert Hazen, one of the lead authors of the study from the Carnegie Institution for Science said.

“It opens the way to using smart sensors on robotic spacecraft, landers and rovers to search for signs of life before the samples return to Earth.”

The new test could help to reveal the history of mysterious ancient rocks on Earth and possibly that of samples already collected by the Mars Curiosity rover’s Sample Analysis at Mars (SAM) instrument – though Dr Hazen admitted the method would need altering for this to work.

“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,” he said.

“We began with the idea that the chemistry of life differs fundamentally from that of the inanimate world; that there are ‘chemical rules of life’ that influence the diversity and distribution of biomolecules.

“If we could deduce those rules, we can use them to guide our efforts to model life’s origins or to detect subtle signs of life on other worlds.

“These results mean that we may be able to find a lifeform from another planet, another biosphere, even if it is very different from the life we know on Earth.

“And, if we do find signs of life elsewhere, we can tell if life on Earth and other planets derived from a common or different origin.

“Put another way, the method should be able to detect alien biochemistries, as well as Earth life.

“That is a big deal because it’s relatively easy to spot the molecular biomarkers of Earth life, but we cannot assume that alien life will use DNA, amino acids, etc.

“Our method looks for patterns in molecular distributions that arise from life’s demand for ‘functional’ molecules.

“What really astonished us was that we trained our machine-learning model to predict only two sample types – biotic or abiotic – but the method discovered three distinct populations: abiotic, living biotic, and fossil biotic.

“In other words, it could tell more recent biological samples from fossil samples – a newly plucked leaf or vegetable, say, versus something that died long ago.

“This surprising finding gives us optimism that other attributes such as photosynthetic life or eukaryotes (cells with a nucleus) might also be distinguished.”

To more simply explain the role of AI in the study, published in the journal Proceedings of the National Academy of Sciences, co-author Dr. Anirudh Prabhu uses the idea of separating coins using different attributes such as their monetary value, metal, year, weight or radius.

“When hundreds of such attributes are involved, AI algorithms are invaluable to collate the information and create highly nuanced insights,” Dr Prabhu said.

“From a chemical standpoint, the differences between biotic and abiotic samples relate to things like water solubility, molecular weights, volatility and so on,” Dr. Jim Cleaves, another lead author, said.

“The simple way I would think about this is that a cell has a membrane and an interior, called the cytosol; the membrane is pretty water-insoluble, while the cell’s content is pretty water-soluble.

“That arrangement keeps the membrane assembled as it tries to minimize its components’ contacts with water and also keeps the ‘inside components’ from leaking across the membrane.

“The inside components can also stay dissolved in water despite being extremely large molecules like chromosomes and proteins.

“So, if one breaks a living cell or tissue into its components, one gets a mix of very water-soluble molecules and very water-insoluble molecules spread across a spectrum.

“Things like petroleum and coal have lost most of the water-soluble material over their long histories.

“Abiological samples can have unique distributions across this spectrum relative to each other, but they are also distinct from the biological distributions.

“The search for extraterrestrial life remains one of the most tantalizing endeavors in modern science.

“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.”

It is now hoped the newly developed AI technique may soon provide answers to the origins of some of Earth’s most mysterious objects.

For example, it could help elucidate the origins of 3.5 billion-year-old black sediments from Western Australia which are hotly debated with some researchers saying they hold Earth’s oldest fossil microbes whilst others claim they are devoid of life signs.

Other such samples from ancient rocks in Northern Canada, South Africa, and China evoke similar debates.

“We’re applying our methods right now to address these long-standing questions about the biogenicity of the organic material in these rocks,” Dr Hazen said.

“If AI can easily distinguish biotic from abiotic, as well as modern from ancient life, then what other insights might we gain?

“For example, could we tease out whether an ancient fossil cell had a nucleus, or was photosynthetic?

“Could it analyze charred remains and discriminate different kinds of wood from an archeological site?

“It’s as if we are just dipping our toes in the water of a vast ocean of possibilities.”

Produced in association with SWNS Talker