Biology subjects

Binder, B. J.

Publications and source records attributed to Binder, B. J..

8 recordsLinked to original sources

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

Quantifying the effects of cell death and agar density on yeast colony biofilms using an extensional flow mathematical model

We use a combination of experiments, mathematical modelling, and parameter estimation to better understand how agar density affects colony biofilm growth of the yeast species Saccharomyces cerevisiae. We obtained 15 total experimental replicates on rectangular plates filled with 0.6%, 0.8%, 1.2%, and 2.0% agar. In the experiments, we measured the horizontal expansion over time, the number of living cells, and the colony-biofilm aspect ratio. These measurements quantify the colony-biofilm size, composition, and shape, respectively. We modelled colony-biofilm expansion using a thin-film extensional-flow mathematical model. By fitting five unknown model parameters to mean experimental data, we show that nutrient uptake decreases and biofilm-substratum adhesion strength increases with an increase in agar density. Sensitivity analysis, fitting to individual replicates, and synthetic-data analysis confirmed that increased biofilm-substratum adhesion is the most consistent effect of increased agar density. This finding aligns with similar results reported for bacteria, and suggests that substratum properties are important for yeast-colony-biofilm growth.

biophysics↗

bioRxiv2025

Multiscale Analysis of Candida albicans Biofilms

Candida albicans is an opportunistic fungal pathogen of significant biomedical concern. Its ability to colonize abiotic surfaces of clinical devices -- such as catheters and airway management systems -- can result in life-threatening sepsis, especially in immunocompromised patients. A deeper understanding of C. albicans biofilm development under different environmental conditions is essential for improving antifungal treatments. In this study, we examine C. albicans biofilm formation using live fluorescence microscopy across multiple scales and modalities, and introduce new quantification approaches. High-magnification tracking of hyphal tips reveals that hyphal elongation occurs intermittently rather than continuously. Using a new automated tracking approach, we show that hyphal emergence is initially rapid and slows down after approximately two hours. At lower magnifications, area coverage across large fields of view proves to be a robust and scalable metric. It is strongly influenced by seed density and extends analysis to later stages of growth. Elevated carbon dioxide levels significantly accelerate area coverage, promoting rapid biofilm expansion. Blue light illumination reduces C. albicans growth in a dose-dependent manner. Light-sheet imaging enables the long-term capture of vertical biofilm growth, complementing widefield-based approaches. We introduce logistic model parameters to effectively quantify the dynamics of surface area growth. The methodologies presented here are well-suited for high-content screening applications aimed at identifying compounds that inhibit or suppress fungal biofilm formation under clinically relevant conditions.

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↗

bioRxiv2025

Modelling the emergence of spiral colony morphology in the yeast Magnusiomyces magnusii

Yeast species have several adaptations that enable them to survive in harsh environments. These adaptations include biofilm formation, where the secretion of extracellular polymeric substances can protect the cells from a hostile environment, or, under nutrient-limited conditions, pseudohyphal or hyphal growth, where the colony can send out long tendrils to explore the environment and seek nutrients. Recently, we observed a spiral colony morphology emerge in an isolate of the hyphae-forming yeast Magnusiomyces magnusii grown under laboratory conditions. We use an off-lattice agent-based model (ABM) that simulates colony development to investigate the hypothesis that bias in the angle between successive hyphal segments causes the spiral morphology. The model involves biologically-motivated rules of hyphal extension, with key model parameters including the colony size at the onset of hyphal filaments, and the angle between the penultimate and the apical segments. Using one example of an experimentally-grown colony, we use a sequential neural likelihood method to perform likelihood-free Bayesian inference to infer the model parameters. Our results indicate a mean angle between hyphal segments of 2.3{whitebullet} [1.1{whitebullet}, 3.6{whitebullet}] (95% credible interval). To confirm the models applicability to colony growth, we use biologically-feasible parameter values to yield morphologies observed in M. magnusii experiments.

microbiology↗

bioRxiv2024

Understanding the mechanisms causing buckling of epithelial monolayers

A model using the rigid body multi-cellular framework (RBMCF) is implemented to investigate the mechanisms of buckling of an epithelial mono-layer. Specifically, the deformation of a monolayer of epithelial cells which are attached to a basement membrane and the surrounding stromal tissue. The epithelial monolayer, supporting basement membrane and stromal tissue are modelled using two separate vertex dynamics models (one for the epithelial monolayer layer and one for the basement membrane and stromal tissue combined) and interactions between the two are considered using the RBMCF to ensure biologically realistic interactions. Model simulations are used to investigate the effects of cell-stromal attachment and membrane rigidity on buckling behaviour. We demonstrate that there are two competing modes of buckling, stromal deformation and stromal separation. HighlightsO_LIA rigid body multi-cellular framework allows for the simulation of an epithelial monolayer which is connected to a basement membrane and surrounding tissue stroma. C_LIO_LIInteraction with basement membrane and tissue stroma allows epithelial cells to migrate forming a confluent monolayer. C_LIO_LIBuckling of monolayer can occur through separation from or deformation of the basement membrane. C_LI

biophysics↗

bioRxiv2021

A Thin-Film Lubrication Model for Biofilm Expansion Under Strong Adhesion

Understanding microbial biofilm growth is important to public health, because biofilms are a leading cause of persistent clinical infections. In this paper, we develop a thin-film model for microbial biofilm growth on a solid substratum to which it adheres strongly. We model biofilms as two-phase viscous fluid mixtures of living cells and extracellular fluid. The model tracks the movement, depletion, and uptake of nutrients explicitly, and incorporates cell proliferation via a nutrient-dependent source term. Notably, our thin-film reduction is two-dimensional and includes the vertical dependence of cell volume fraction. Numerical solutions show that this vertical dependence is weak for biologically-feasible parameters, reinforcing results from previous models in which this dependence was neglected. We exploit this weak dependence by writing and solving a simplified one-dimensional model that is computationally more efficient than the full model. We use both the one and two-dimensional models to predict how model parameters affect expansion speed and biofilm thickness. This analysis reveals that expansion speed depends on cell proliferation, nutrient availability, cell-cell adhesion on the upper surface, and slip on the biofilm-substratum interface. Our numerical solutions provide a means to qualitatively distinguish between the extensional flow and lubrication regimes, and quantitative predictions that can be tested in future experiments.

biophysics↗

bioRxiv2021

A rigid body framework for multi-cellular modelling

Multi-cellular modelling, where tissues and organs are represented by a collection of individual interacting agents, is a well established field, encapsulating several different approaches. In particular, off-lattice models, which represent cells using points that are free to move in space, have been applied to numerous biological problems in both two and three dimensions. The fact that a cell can be represented by point objects is useful in a wide range of settings, particularly when large populations are involved. However, a purely point-based representation is not naturally equipped to deal with objects that inherently have length like cell boundaries or external membranes. In this paper we introduce a novel off-lattice modelling framework that exploits rigid body mechanics to represent cells using a collection of one-dimensional edges (rather than zero-dimensional points) in a viscosity-dominated system. The rigid body framework can be used, among other things, to represent cells as free moving polygons, to allow epithelial layers to smoothly interact with themselves, and to model rod shaped cells like bacteria. We demonstrate the value of our new framework by using it in these three applications, showing that this approach can replicate established results, as well as offer solutions to problems that limit the scope of current off-lattice multi-cellular models.

systems biology↗