Biology subjects

Sun, T. A.

Publications and source records attributed to Sun, T. A..

3 recordsLinked to original sources

bioRxiv2026

How far can microbial monocultures predict growth in multi-strain communities?

Previous research in microbes successfully predicted biculture growth based on monoculture growth curves. Still, the usual model-based approach does not seem to extend to communities involving more than two bacterial strains. Here, we use a model-blind machine-learning approach to predict community-wide yield, area under the growth curve, maximum relative growth rate and its timing in communities involving up to five strains of Escherichia coli. First, we identify the highest per-capita growth rate and its timing in monocultures as major predictors of multi-strain community growth. Next, we show that a random forest trained on communities involving a low number of strains is able to predict the outcomes of communities involving a higher number of strains. Finally, we reveal diminishing returns in using more and more complex communities in order to predict the behaviour of a higher-level community, because monoculture- and biculture-based growth predictions are often already accurate. This finding relativises the need for experiments involving high numbers of strains when studying the growth dynamics of multi-strain communities.

ecology↗

bioRxiv2025

Data-driven symbolic regression reveals microbial growth laws

O_LIConnecting mathematical models with empirically measured microbial growth has remained challenging, as numerous competing models based on different theoretical approaches can fit observations. Therefore, we develop a method to automatically propose growth models from microbial data alone. We validate this approach using an available dataset of E. coli grown on known resources, and study sixteen species across various concentrations of a rich medium. C_LIO_LIThe inherently interpretable approach of symbolic regression infers explicit dynamical models directly from growth data. Using symbolic regression natively, does not favour biologically interpretable models, but we find cumulative population gain to be a more informative machine learning feature than population size. C_LIO_LIRandom Forest machine learning allows us to relate this finding to the approximation of a constant-rate per capita resource consumption. This suggests that the area under the growth curve (AUC) measured in routine experiments provides information on the effective resource dynamics governing microbial growth. Finally, we use theoretical insights to inform the symbolic regression algorithm and favour biologically interpretable models. C_LIO_LIOverall, we found that balancing between data fit, parsimony and biological relevance favoured both the simplest, linear approximation, and models based on a Monod dynamics, with either one or two underlying resources. Therefore, our approach to read growth laws off of microbial batch cultures provides insights on data-driven modelling. C_LI

ecology↗

bioRxiv2022

Distribution of mutation rates challenges evolutionary predictability

Natural selection is commonly assumed to act on extensive standing genetic variation. Yet, accumulating evidence highlights the role of mutational processes creating this genetic variation: to become evolutionarily successful, adaptive mutants must not only reach fixation, but also emerge in the first place, i.e. have a high enough mutation rate relative to the population size. Here, we use numerical simulations to investigate how mutational biases impact our ability to observe rare mutational pathways in the laboratory and to predict outcomes in experimental evolution. We find the number of available mutations and the unevenness of their mutation rates to increase, sometimes impractically, the experimental effort required to discover all of them. Thus, most experimental studies lack power to directly observe the full range of adaptive mutations. Modelling mutation rates as a distribution, we show that a substantially larger target size ensures that a pathway mutates more commonly. Therefore, we predict that commonly mutated pathways are conserved between closely related species, contrary to rare pathways. This approach formalizes our proposal that most mutations have a lower mutation rate than the average mutation rate measured experimentally. We suggest that the extent of genetic variation is overestimated when based on the average mutation rate.

evolutionary biology↗