Selective Nutrient Transport in Bacteria: Multicomponent Transporter Systems Reign Supreme

Multicomponent transporters are used by bacteria to transport a wide range of nutrients. These systems use a substrate-binding protein to bind the nutrient with high affinity and then deliver it to a membrane-bound transporter for uptake. Nutrient uptake pathways are linked to the colonisation potential and pathogenicity of bacteria in humans and may be candidates for antimicrobial targeting. Here we review current research into bacterial multicomponent transport systems, with an emphasis on the interaction at the membrane, as well as new perspectives on the role of lipids and higher oligomers in these complex systems.

A higher-order finite element reactive transport model for unstructured and fractured grids

This work presents a new reactive transport framework that combines a powerful geochemistry engine with advanced numerical methods for flow and transport in subsurface fractured porous media. Specifically, the PhreeqcRM interface (developed by the USGS) is used to take advantage of a large library of equilibrium and kinetic aqueous and fluid-rock reactions, which has been validated by numerous experiments and benchmark studies. Fluid flow is modeled by the Mixed Hybrid Finite Element (FE) method, which provides smooth velocity fields even in highly heterogenous formations with discrete fractures. A multilinear Discontinuous Galerkin FE method is used to solve the multicomponent transport problem. This method is locally mass conserving and its second order convergence significantly reduces numerical dispersion. In terms of thermodynamics, the aqueous phase is considered as a compressible fluid and its properties are derived from a Cubic Plus Association (CPA) equation of state. The new simulator is validated against several benchmark problems (involving, e.g., Fickian and Nernst-Planck diffusion, isotope fractionation, advection-dispersion transport, and rock-fluid reactions) before demonstrating the expanded capabilities offered by the underlying FE foundation, such as high computational efficiency, parallelizability, low numerical dispersion, unstructured 3D gridding, and discrete fraction modeling.