Biology subjects

Cylke, A.

Publications and source records attributed to Cylke, A..

3 recordsLinked to original sources

bioRxiv2025

Cell Geometry Limits Bacterial Metabolic Efficiency

Bacterial metabolic strategies are fundamentally linked to their physical form, yet a quantitative understanding of how cell size and shape constrain the efficiency of biomass production remains poorly understood. Here, we develop a coarse-grained whole-cell model of bacterial physiology that integrates proteome allocation, metabolic fluxes, and cell geometry with physical limits on cell surface area and intracellular diffusion. Our model shows that the efficiency of cellular growth is not monotonic with nutrient availability; instead, it peaks precisely at the onset of overflow metabolism, framing this metabolic switch as an optimal trade-off between efficient use of imported nutrients and rapid growth. By simulating perturbations to cell morphology, we demonstrate the strong metabolic advantage of a high surface-to-volume ratio, which consistently improves growth efficiency. Finally, we show how geometric limits on growth efficiency result in a hard physical constraint: the maximum sustainable cell size is inversely related to the growth rate. This is due to a fundamental conflict between the proteomic cost of growth speed and the cost of size, which creates a budget crisis in large, fast-growing cells. Our work shows how a few physical rules define the allowable strategies for bacterial metabolism and provides a mechanistic explanation for the observed limits on microbial cell size and growth.

systems biology↗

bioRxiv2025

Mechanistic basis for non-exponential bacterial growth

Bacterial populations typically exhibit exponential growth under resource-rich conditions, yet individual cells often deviate from this pattern. Recent work has shown that the elongation rates of Escherichia coli and Caulobacter crescentus increase throughout the cell cycle (super-exponential growth), while Bacillus subtilis displays a mid-cycle minimum (convex growth), and Mycobacterium tuberculosis grows linearly. Here, we develop a single-cell model linking gene expression, proteome allocation, and mass growth to explain these diverse growth trajectories. By calibrating model parameters with experimental data, we show that DNA-proportional mRNA transcription produces near-exponential growth, whereas deviations from this proportionality yield the observed non-exponential growth patterns. Analysis of gene expression perturbations reveals that ribosome expression primarily controls dry mass growth rate, whereas envelope expression more strongly affects cell elongation rate. Fitting our model to single-cell experimental data reproduces convex, super-exponential, and linear modes of growth, demonstrating how envelope and ribosome expression schedules drive cell-cycle-specific behaviors. These findings provide a mechanistic basis for non-exponential single-cell growth and offer insights into how bacterial cells dynamically regulate elongation rates within each generation.

biophysics↗

bioRxiv2024

Energy allocation theory for bacterial growth control in and out of steady state

Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation which describes cellular growth dynamics based on partitioning of metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size, and predicts nutrient-dependent trade-offs between energy expended for growth, division, and shape maintenance. By calibrating model parameters with available experimental data for the model organism E. coli, our model is capable of describing bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. The model captures these perturbations with minimal added complexity and our unified approach predicts the driving factors behind a wide range of observed morphological and growth phenomena.

biophysics↗