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July 14, 2025

Peaceful microbes outlast aggressive rivals in harsh, frequently disrupted environments

Phase portraits of the Durrett-Levin model (S2.1) with decreasing death rates. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2424372122
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Phase portraits of the Durrett-Levin model (S2.1) with decreasing death rates. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2424372122

On the battlefield that is the microbial world, where microorganisms often try to wipe out the competition by producing various toxins, sometimes it helps to be a pacifist.

Cornell researchers with the Center for Applied Mathematics have found that peaceful microbes are more likely to thrive, and their more aggressive peers perish, if their environment is harsh or experiences violent disruptions.

The were published in Proceedings of the National Academy of Sciences. The paper's lead author is MingYi Wang, M.S. '21, Ph.D. '24.

"The big overarching question was: When is it advantageous to be antagonistic, and when is it detrimental?" said Andrea Giometto, assistant professor of civil and in Cornell Engineering and a co-senior author of the paper.

"Microbes often pay a metabolic cost to display antagonistic behavior, so they have to invest some energy into that. Especially when there are opportunities for growth and still very few microbes around, it might actually be better to redirect that energy toward dividing faster."

The project got its start when Wang, then a doctoral student in , took Giometto's course Stochastic Modeling of Complex Systems, which used examples from biology and to explore processes in which randomness plays an essential role.

Time-progressive movie of sensitives vs (myopic) population-sensing killers under regular dilutions (T = 1) with no basal mortality (δ = 0). Each frame contains two panels of contour plots with all possible states (f,N). Left: the value function u(x, y, t), which encodes the best pre-dilution killer fraction at time t. Right: the optimal feedback toxin-on/off policy a∗ (x, y, t). Still frame corresponding to t = 0 is shown above.

Wang also involved his Ph.D. adviser, Alexander Vladimirsky, professor of mathematics in the College of Arts and Sciences.

"He came back to me saying, 'Hey, there is this wonderful area where our mathematical tools could be useful,'" said Vladimirsky, who is a co-senior author of the paper. "That started Andrea and me talking."

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Microbial antagonism helps microbes establish themselves and outcompete rivals, a strategy that can benefit their hosts by protecting against pathogens, and can help humans more broadly by providing antimicrobial compounds to fight infections. But how that competition plays out can vary depending on where, when and for how long it takes place.

Antagonistic microbes tend to win out against their less aggressive counterparts in undisturbed laboratory settings, but the opposite often occurs when the environment is fluctuating. That can include environments that are mostly accommodating but subject to frequent violent disruptions, such as when the gut microbiome is perturbed by a course of antibiotics or a person brushes the plaque from their teeth.

The researchers set out to reproduce these latter "boom-and-bust" dynamics. Every day, the team grew a "killer" and a "sensitive" strain of Saccharomyces cerevisiae, the budding yeast, and then diluted them and measured how many cells of each strain had survived. Then they repeated the cycle again and again.

"What we see is that when the environment gets diluted very frequently—in our case, every day—the non-antagonistic cells are better off than when they're diluted more seldom, which for us was every 48 hours," Giometto said.

The team's computational modeling addressed questions that were difficult to answer through biological experiments, such as the impact of different dilution strengths and frequencies, randomness versus predictability in dilution times, and different toxin-production strategies for antagonistic microbes. The modeling also highlighted the importance of certain antagonists' "strategies," such as quorum sensing, which allows them to engage in combat in response to specific environmental or microbial cues.

"When the dilution times are random, the antagonists could get quite a substantial benefit from being selective about when to produce toxin," Vladimirsky said. "In contrast, when the dilutions happen right on schedule, our computational experiments showed that the benefit they could derive from being selective is much smaller."

Understanding the trade-offs between the metabolic cost of antagonism and its competitive benefits may inform the future design of probiotics that can effectively fight infections by tuning their aggressive behavior in response to both community composition and environmental conditions.

There might, perhaps, be a lesson here for a broader range of competitors—from microbes to animals, people and organizations.

"In colloquial words, if you're too focused on spiting others, that takes away from time and energy you could spend on becoming better yourself or producing more offspring," Vladimirsky said.

"Our research shows that whether the antagonistic behavior pays off in the long run is a subtle question. The answer is not fully determined by the types and quantity of competitors and the time-averaged properties of the competitive environment. It is also strongly influenced by how that environment varies over time."

More information: MingYi Wang et al, Overcoming toxicity: How nonantagonistic microbes manage to thrive in boom-and-bust environments, Proceedings of the National Academy of Sciences (2025).

Journal information: Proceedings of the National Academy of Sciences

Provided by Cornell University

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In environments that are harsh or frequently disrupted, non-antagonistic microbes outcompete aggressive, toxin-producing strains. The metabolic cost of antagonism becomes disadvantageous under such fluctuating conditions, favoring microbes that invest energy in growth rather than competition. Environmental variability, including disruption frequency and predictability, strongly influences these outcomes.

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