bioRxivEvolutionary BiologyPreprint
No Trade-Offs Required: Cross-Feeding From Survival Alone
Abstract
Cross-feeding relationships shape the composition of many microbial communities, yet the evolutionary processes that give rise to them remain poorly understood. Most theoretical and experimental work has therefore focused on minimal scenarios, particularly the stable cross-feeding polymorphisms that evolve in asexual populations growing on a single energy source (Helling et al., 1987). Yet replicate experiments do not always produce cross-feeding populations, raising the question of why genetically identical populations evolving under identical conditions can follow different evolutionary trajectories (Treves et al., 1998). Here we present a bare-bones agent-based model of evolution in a chemostat. We show that selection for energy acquisition alone is sufficient to promote the evolution of cross-feeding, without invoking mechanisms specific to metabolic exchange. The resulting communities nevertheless differ across replicate simulations, reproducing the qualitative variability observed experimentally. Significance StatementMicrobial communities often depend on cross-feeding, in which one cells metabolic product becomes anothers energy source. Existing explanations typically invoke trade-offs between metabolic tasks or other mechanisms specific to cross-feeding itself. Using large-scale in silico simulations of evolution in a chemostat, we show that no such explanation is required. A population that competes for metabolic energy by utilizing a primary resource and then releasing a product that may itself serve as an energy source can evolve into a mixed population of organisms that specialize in the primary resource alongside others that specialize in the secondary one. Energy-based probabilistic death and reproduction are sufficient to produce this coexistence and to reproduce the mixed outcomes seen in laboratory evolution experiments.
- research focus: synaptic & neural circuits
- research focus: host-pathogen & immune response
- research focus: chromatin & epigenetic regulation
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
- Citations
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