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Seyed-Allaei, H.

Publications and source records attributed to Seyed-Allaei, H..

2 recordsLinked to original sources

A quantitative framework for bacterial competition during starvation

Bacterial communities often spend long periods under starvation, where survival depends not only on their intrinsic ability to withstand stress but also on nutrients released by dying neighbors. This creates a distinct form of competition: cells compete for recycled necromass, and the outcome should depend on physiological traits that determine nutrient uptake and maintenance demand. Here, we develop a quantitative framework for this competition using Escherichia coli populations whose starvation physiology is tuned by prior growth history. Fast-grown populations have higher maintenance demands and die slightly faster in monoculture, whereas slow-grown populations are better adapted to starvation. In co-culture, these physiological differences are strongly amplified in a frequency-dependent manner: less-adapted populations die several-fold faster than in monoculture, whereas well-adapted populations can reduce their death rate below that of stationary-phase adapted monocultures. We explain these dynamics with a shared-energy-pool model in which death releases recyclable nutrients, surviving cells consume them for maintenance, and intracellular energy sets death rate. Using independently measured parameters, the model makes parameter-free predictions for competitive survival. The predicted instantaneous death rates collapse onto a universal function of the population ratio over four orders of magnitude. Our results establish necromass recycling as a quantitative basis for bacterial competition during starvation and lay the foundation for modeling communities during starvation.

systems biology↗

Force Generation by Enhanced Diffusion in Enzyme-Loaded Vesicles

Recent experiments have shown that the diffusion coefficient of some metabolic enzymes increases with the concentration of their cognate substrate, a phenomenon known as enhanced diffusion. In the presence of substrate gradients, enhanced diffusion induces enzymatic drift, resulting in a non-homogeneous enzyme distribution. Here, we study the effects of enhanced diffusion on enzyme-loaded vesicles placed in external substrate gradients using a combination of computer simulations and analytical modeling. We observe that the spatially inhomogeneous enzyme profiles generated by enhanced diffusion result in a pressure gradient across the vesicle, which leads to macroscopically observable effects, such as deformation and self-propulsion of the vesicle. Our analytical model allows us to characterize dependence of the velocity of propulsion on experimentally tunable parameters. The effects predicted by our work provide an avenue for further validation of enhanced diffusion, and might be leveraged for the design of novel synthetic cargo transporters, such as targeted drug delivery systems.

biophysics↗