Sundstrom, J. F. | Search bioRxiv

bioRxiv2026

Understanding the impact of sodium sulfide on the invasive growth of wine yeast

Yeasts ability to invade surfaces has important implications for infections and food contamination. Invasive growth in yeast is influenced by genetic and environmental factors. In this exploratory study, we used a systematic experimental design to identify conditions under which sulfide-induced invasive growth can be reliably observed and quantified and investigated the effects of sulfide, gene deletions, and environmental conditions on the invasive behaviour of the wine yeast strain AWRI 796. Sulfide enhanced invasion in the (parent) AWRI 796 strain under nitrogen-limiting conditions, although its effect was obscured by experimental variability and pre-culture conditions. Genetic factors had a major effect on the overall invasive phenotype, with deletion of key genes suppressing invasion. Most gene-deletion mutants did not significantly affect how the colony responded to sulfide. In addition to sulfide and genotype, environmental conditions also influenced invasive behaviour. The pre-2xSLAD pre-culture condition was best for detecting sulfide-induced growth, and later plate washing time and decreased nutrient levels enhanced invasiveness. Our experimental design and findings provide a framework for understanding the determinants of yeast invasiveness, which may inform future studies on filamentous yeast behaviour.

microbiology↗

bioRxiv2025

Agent-based modelling and time series inference of filamentous yeast colonies

The bakers yeast Saccharomyces cerevisiae can form invasive filamentous colonies with non-uniform spatiotemporal patterns. We aimed to better understand how individual cellular actions give rise to colony-scale patterns. We used an off-lattice agent-based model to simulate colony growth, and used a neural likelihood estimator (NLE) to infer parameters for experimental photographs. Li et al. [1] used approximate Bayesian computation (ABC) to infer parameters using coarse summary statistics obtained from a single time point averaged across experimental replicates. The NLE overcomes the computational expense of ABC, allowing us to infer the parameters of individual colonies from a full time series of experimental photographs. To demonstrate the capabilities of our model and inference technique, we tested extensively on synthetic data and then predicted yeast growth under three different experimental conditions. As before, the proportion of total colony growth above which pseudohyphal growth is permitted was a key parameter that contributed to colony morphology. Since our NLE-based approach incorporates time series data, it yielded better parameter estimates and more accurate predictions compared to our ABC-based method. This updated approach improved understanding of how the probability that a stated cell produces a pseudohyphal cell influences colony morphology. In this way, the model also has the potential to generate hypotheses, which can be tested through biological experiments to increase the understanding of the basis for different growth patterns in yeast.

cell biology↗