
Abstract Experimental studies of microbial communities routinely reveal that they have multiple stable states. While each of these states is generally resilient, certain perturbations such as antibiotics, probiotics and diet shifts, result in transitions to other states. Can we reliably both predict such stable states as well as direct and control transitions between them? Here we present a new conceptual model — inspired by the stable marriage problem in game theory and economics — in which microbial communities naturally exhibit multiple stable states, each state with a different species’ abundance profile. Our model’s core ingredient is that microbes utilize nutrients one at a time while competing with each other. Using only two ranked tables, one with microbes’ nutrient preferences and one with their competitive abilities, we can determine all possible stable states as well as predict inter-state transitions, triggered by the removal or addition of a specific nutrient or microbe. Further, using an example of 7 Bacteroides species common to the human gut utilizing 9 polysaccharides, we predict that mutual complementarity in nutrient preferences enables these species to coexist at high abundances.
FOS: Computer and information sciences, Molecular Networks (q-bio.MN), Populations and Evolution (q-bio.PE), FOS: Physical sciences, Gastrointestinal Microbiome, Gastrointestinal Tract, Computer Science - Computer Science and Game Theory, Polysaccharides, Biological Physics (physics.bio-ph), FOS: Biological sciences, Bacteroides, Humans, Quantitative Biology - Molecular Networks, Physics - Biological Physics, Quantitative Biology - Populations and Evolution, Symbiosis, Computer Science and Game Theory (cs.GT)
FOS: Computer and information sciences, Molecular Networks (q-bio.MN), Populations and Evolution (q-bio.PE), FOS: Physical sciences, Gastrointestinal Microbiome, Gastrointestinal Tract, Computer Science - Computer Science and Game Theory, Polysaccharides, Biological Physics (physics.bio-ph), FOS: Biological sciences, Bacteroides, Humans, Quantitative Biology - Molecular Networks, Physics - Biological Physics, Quantitative Biology - Populations and Evolution, Symbiosis, Computer Science and Game Theory (cs.GT)
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
