{"id":5072,"date":"2026-04-14T14:38:09","date_gmt":"2026-04-14T14:38:09","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/5072\/"},"modified":"2026-04-14T14:38:09","modified_gmt":"2026-04-14T14:38:09","slug":"ai-spots-hidden-behavior-patterns-in-self-organizing-bacteria-2","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/5072\/","title":{"rendered":"AI spots hidden behavior patterns in self-organizing bacteria"},"content":{"rendered":"

<\/p>\n

Jiangguo Zhang and Oleg Igoshin. Credit: Jared Jones\/Rice University<\/p>\n

Life moves in mysterious ways\u2014and perhaps especially so for organisms that undergo dramatic shifts in levels of self-organization, such as Myxococcus xanthus. A custom-built artificial intelligence system developed by Rice University researchers helped uncover how bacterial communities organize themselves, showing that the earliest moments of a biological transition carry far more information than previously considered.<\/p>\n

The findings, reported in Proceedings of the National Academy of Sciences<\/a>, bring new insight into how genotype, an organism’s genetic blueprint, gives rise to phenotype, its looks and behavior.<\/p>\n

M. xanthus is a soil-dwelling microbe that lives in colonies of rod-shaped cells held together by slime, roaming about in predatory swarms that feast on other microbes and decaying organic debris.<\/p>\n

But when there is no food around and the colony is sufficiently large, the rod-shaped cells stop their roaming and turn toward each other, merging into a mounded structure called a fruiting body: no longer a predatory pack of look-alikes but a single, distinct organism with differentiated features.<\/p>\n

Within these fruiting bodies, some cells sacrifice themselves, while others transform into hardy spores capable of surviving harsh conditions until food is again available. This transformation from thousands of independent cells into a single complex structure takes place without any master plan or guidance from a central command site. The process has long been a source of fascination for scientists and amateur naturalists alike.<\/p>\n

“Understanding this process has been difficult because the patterns formed by these cells are complex and constantly changing,” said Oleg Igoshin, a professor of bioengineering and biosciences at Rice.<\/p>\n

Movie comparing Myxococcus xanthus strains with different motility systems: wild type, impaired social motility and impaired adventurous motility. Time-lapse microscopy shows how self-organization patterns diverge during development, while a custom deep-learning model maps each trajectory into a shared feature space and pairs each frame with AI-generated reconstructions. Credit: Jiangguo Zhang\/Rice University <\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAI tracks a microbe’s shape\u2011shifting<\/p>\n

To track how the transformation from swarm to fruiting body unfolds\u2014and how genetic differences between bacterial communities shape the outcome\u2014Igoshin and his team<\/p>\n

developed a custom deep-learning framework that can turn time-lapse microscopy images of developing bacterial communities into a simple numerical description of their overall pattern.<\/p>\n

“This approach has allowed us to compare different bacterial behaviors in a precise and quantitative way,” said Jiangguo Zhang, a Rice doctoral alumnus and first author on the study.<\/p>\n

The team recorded more than 900 time\u2011lapse “movies” showing how 292 different strains of myxobacteria self-organize over 24 hours, with snapshots taken every minute. The time-lapse movies’ resolution is not fine-grained enough for individual cells to be visible, but the images revealed how local cell densities shifted as swarms reorganized into aggregates and, in some cases, mature fruiting bodies.<\/p>\n

The resulting dataset was massive, with each image containing millions of pixels. To analyze it, the researchers built a bespoke AI system<\/a> consisting of three parts.<\/p>\n

First, a deep image encoder compresses each frame into a concise numerical schema, i.e. a set of 13 values that summarize the overall spatial pattern. A generative model then reconstructs realistic images from these schemata, while a contrastive network learns to distinguish meaningful biological difference from irrelevant variation.<\/p>\n

“Unlike traditional methods, the system did not rely on human insights but rather learned the best set of numbers to characterize these patterns automatically,” said Zhang, who now works as a machine learning engineer for YouTube.<\/p>\n

<\/p>\n

Jiangguo Zhang and Oleg Igoshin. Credit: Jared Jones\/Rice University<\/p>\n

\n Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights.
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\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tRevealing hidden order in early chaos<\/p>\n

By comparing thousands of image pairs, the AI learned to focus on the underlying biological pattern, laying bare subtle signatures of self-organization that would have been extremely difficult for a human observer to notice.<\/p>\n

This helped upend a long-held belief that after starvation begins, bacterial populations enter a kind of preparatory phase characterized by chaotic movement.<\/p>\n

“We used a custom deep-learning approach to make the invisible visible and the qualitative measurable,” said Ankit Patel, an assistant professor in the Department of Electrical and Computer Engineering at Rice and the Department of Neuroscience at Baylor College of Medicine and a study co-author.<\/p>\n

“This new level of quantitative precision is exactly what’s needed to unlock the complex relationship between an organism’s genes and its eventual behavior.”<\/p>\n

Igoshin said the approach revealed that “hidden spatial patterns present at the very beginning of development already contain clues about how the community will organize itself hours later.”<\/p>\n

The model could determine whether a population would successfully form aggregates with about 80\u201385% accuracy, even when images taken immediately after the onset of starvation appeared almost identical to the human eye. It also helped shed light on how specific genetic mutations alter multicellular behavior.<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tLinking genetic tweaks to group behavior<\/p>\n

“This organism is a perfect case study for examining the genetic basis of multicellular self-organization and its dynamics because it has two main motility systems\u2014known as ‘social’ and ‘adventurous’ motility\u2014each governed by an independent set of genes,” Igoshin said.<\/p>\n

Mutations impacting either of these two movement mechanisms result in different developmental trajectories. One strain exhibiting impaired social motility failed to coalesce into a fruiting body, aggregating instead into multiple thinner collectives after about 18 hours; another strain carrying a mutation in a gene crucial for adventurous motility produced a large, irregular, translucent structure with increasingly pronounced morphological distortions.<\/p>\n

Some mutants never formed aggregates at all, while others began the process but stalled partway through.<\/p>\n

By translating each of these trajectories into the same low-dimensional feature space, the model allowed direct comparisons across strains, revealing how different genetic perturbations map onto distinct patterns of self-organization.<\/p>\n

“Our approach in this work provides a powerful new way to measure and study complex biological patterns,” Igoshin said.<\/p>\n

Additional co-authors include Patrick Murphy from Rice; Eduardo Caro, Peiying Chen, Troporsha Tasnim Khan and Roy Welch from Syracuse University; and Lawrence Shimkets from the University of Georgia.<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\tPublication details\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

Jiangguo Zhang et al, Deep learning framework for quantifying self-organization in Myxococcus xanthus, Proceedings of the National Academy of Sciences (2026). DOI: 10.1073\/pnas.2532223123<\/a><\/p>\n

\n\t\t\t\t\t\t\t\t\t\t\t\t\tProvided by
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tRice University<\/a>
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

\n\t\t\t\t\t\t\t\t\t\t\t\tCitation:
\n\t\t\t\t\t\t\t\t\t\t\t\tAI spots hidden behavior patterns in self-organizing bacteria (2026, April 14)
\n\t\t\t\t\t\t\t\t\t\t\t\tretrieved 14 April 2026
\n\t\t\t\t\t\t\t\t\t\t\t\tfrom https:\/\/phys.org\/news\/2026-04-ai-hidden-behavior-patterns-bacteria.html\n\t\t\t\t\t\t\t\t\t\t\t <\/p>\n

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