Biology subjects

Kiciatovas, D.

Publications and source records attributed to Kiciatovas, D..

4 recordsLinked to original sources

bioRxiv2025

Normative assembly rule reveals fairness in microbial communities

Understanding and predicting how communities assemble is a paramount challenge in ecology. Here we address these questions normatively by comparing the ecological distribution of growth surplus to a game-theoretically fair distribution based on each species Shapley value. By analyzing in total 56 distinct community outcomes, we assess how fairly biomass is distributed in microbial communities displaying both competitive and cooperative interactions in different environmental conditions. We find examples of fair communities that closely follow their Shapley value across all environments as well as counterexamples where the true abundances deviate from the species objective contribution to community biomass. Our results give unique empirical insights into the distributive function of ecological dynamics and lay down the theoretical foundations of what might become a normative community assembly theory.

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↗

bioRxiv2025

Universal bacterial clade dynamics dominate under predation despite altered phenotypes and mutation targets

Recent studies have revealed bacterial genome-wide evolution to be complex and dynamic even in a constant environment, characterized by the emergence of new clades competing or temporarily coexisting as each clade undergoes evolutionary change. Previous studies on predator-prey dynamics tracking simple ecological and phenotypic metrics have shown predation to fundamentally alter prey evolution, facilitating defense evolution followed by coevolution and frequency dependent selection between defended and undefended prey genotypes. Here we sought to consolidate these fields by examining genome-wide evolution in five bacterial prey species separately subjected to long-term evolution under ciliate predation. We hypothesized that the presence of predation could change the pattern of clonal dynamics, for example, by more frequently producing selective sweeps if predation-defense-related mutations are under strong selection. For all species, we found mutational signals of prey adaptation, with phenotypic data and genomic mutation targets demonstrating changes in composition between the experimental treatments. Intriguingly, despite higher variant counts, overall temporal clade dynamics across the coevolved prey species were strikingly similar to those of bacteria evolving alone, with constant emergence, competition and quasi-stable coexistence of clades. This study shows that long-term molecular evolution in bacterial prey under predation is more interesting and less predictable than we might expect based on existing coevolutionary theories.

evolutionary biology↗

bioRxiv2023

Quantifying massively parallel microbial growth with spatially mediated interactions

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterized at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to an interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments, i.e. interpopulation interactions are mediated via the shared environment. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses. Author summaryImage-based platforms allow obtaining population size estimates for massively parallel growth experiments on substrate plates at relatively low cost. However, such population size data has been shown to display a high degree of spatial variability, which occurs even with isogenic populations. Here we first quantified the importance of spatial location on growth variation using a machine learning approach, and then developed spatially aware population growth models to explain the spatial structure of the growth data. Ultimately, we showed that a spatial consumer-resource model with local microhabitats connected via diffusion can fully explain the observed spatial variation in growth while allowing the inference of intrinsic growth parameters of specific populations. This result provides a method for systematic extraction of spatial growth models and paves the way for massively parallel eco-evolutionary experimentation.

ecology↗