Biology subjects

Larkin, J. W.

Publications and source records attributed to Larkin, J. W..

3 recordsLinked to original sources

bioRxiv2024

An Agent-Based Model of Metabolic Signaling Oscillations in Bacillus subtilis Biofilms

Microbes of nearly every species can form biofilms, communities of cells bound together by a self-produced matrix. It is not understood how variation at the cellular level impacts putatively beneficial, colony-level behaviors, such as cell-to-cell signaling. Here we investigate this problem with an agent-based computational model of metabolically driven electrochemical signaling in Bacillus subtilis biofilms. In this process, glutamate-starved interior cells release potassium, triggering a depolarizing wave that spreads to exterior cells and limits their glutamate uptake. More nutrients diffuse to the interior, temporarily reducing glutamate stress and leading to oscillations. In our model, each cell has a membrane potential coupled to metabolism. As a simulated biofilm grows, collective membrane potential oscillations arise spontaneously as cells deplete nutrients and trigger potassium release, reproducing experimental observations. We further validate our model by comparing spatial signaling patterns and cellular signaling rates with those observed experimentally. By oscillating external glutamate and potassium, we find that biofilms synchronize to external potassium more strongly than to glutamate, providing a potential mechanism for previously observed biofilm synchronization. By tracking cellular glutamate concentrations, we find that oscillations evenly distribute nutrients in space: non-oscillating biofilms have an external layer of well-fed cells surrounding a starved core, whereas oscillating biofilms exhibit a relatively uniform distribution of glutamate. Our work shows the potential of agent-based models to connect cellular properties to collective phenomena and facilitates studies of how inheritance of cellular traits can affect the evolution of group behaviors.

microbiology↗

bioRxiv2024

Non-Optical, Label-free Electrical Capacitance Imaging of Microorganisms

Many fundamental insights into microbiology have come from imaging, which is typically synonymous with optical techniques. However, the sample preparation needed for many optical microscopy methods such as labeling, fixing, or genetic modification, limits the range of species and environments we can investigate. Here we demonstrate the use of electrical capacitance measurements as a non-optical method for imaging live microbial samples. In electrical capacitance imaging (ECI), samples are positioned in contact with a semiconductor sensor array, and localized capacitance measurements are made across the array. From these measurements, we generate textured images of a variety of microbial colonies. We determine that capacitance is correlated with local sample thickness by comparing ECI data to 3D confocal scans. We further illustrate with ECI that a difference in capacitance signal allows microbial species to be spatially distinguished in co-culture conditions. In order to highlight the versatility of our system, we capture the cross-sectional development of floating pellicle biofilms in a liquid culture at millimeter length scales during weeks-long time-lapse experiments. These novel results establish a new low-cost and portable platform which can be used for spatially and temporally resolved experiments in diverse environments with a wide variety of microbial species. IMPORTANCEMicrobes live in diverse environments, and occupy biological roles across many timescales. Investigating the full scope of microbial activity requires imaging systems appropriate to each context. Though optical microscopy is powerful, the use of light, lenses, and other hardware limits where it can be applied. At the same time, existing non-optical imaging methods are frequently destructive to samples and require extensive equipment. In this paper we present a non-optical imaging system that is small, cheap, requires no sample labeling, and is compatible with a variety of microbial species. Our system uses semiconductor chips to measure the inherent material properties of a sample with spatial sensitivity, producing images of microbes contrasted against their environment and each other. Our technique captures label-free, micrometer-resolution images with a pocket-sized device, enabling microbiological imaging experiments in new environments with new species.

microbiology↗

bioRxiv2022

Computational model of fractal interface formation in bacterial biofilms

Bacteria benefit from cellular heterogeneity: cells differentiate into diverse gene expression states. As colonies grow, cellular phenotypes arrange into spatial patterns. To uncover the functional role of these emergent patterns, we must understand how they arise from cellular growth and mechanical interactions. Here we present a simple, agent-based model to predict patterns of motile and extracellular matrix-producing cells in biofilms of Bacillus subtilis. By incorporating phenotypic inheritance, mechanical interactions, and peripheral motile cell dispersal, our model predicts the emergence of a pattern: matrix cells surround a fractal-like interior motile population. We find that, while some properties of the motile-matrix interface depend on initial conditions, the motile distribution at large radii depends solely on the models growth mechanism. The phenotypic interface exhibits a fractal dimension that increases as biofilms grow but reaches a maximum as the peripheral layer of matrix cells exceeds the capacity of the inner cells to push it out of the way. By varying parameters, we find correlations between the interface fractal dimension and expansion of motile cells. We validate findings using experiments on B. subtilis biofilms in microfluidics. Our model demonstrates the emergence of colony-level phenotypes from single cell-level interactions and cells modifying their own environment.

biophysics↗