Intestinal mucosal block is the transient reduction in iron absorption ability of intestinal epithelial cells (enterocytes) in response to previous iron exposures that occur at the cell scale. The block characteristics have been shown to depend both on iron exposure magnitude and temporality, and understanding block control will enable deeper understanding of how intestinal iron absorption contributes to pathological iron states. Three biochemical mechanisms implicated in driving the block behavior are divalent metal transporter 1 endocytosis, ferritin iron sequestration, and iron regulatory protein regulation of iron related protein expression. In this work, a model of enterocyte iron metabolism is built based on published experimental data that is capable of reproducing the mucosal block phenomena. The model is then used to estimate the quantitative contribution of each of the three mechanisms on the properties of the mucosal block. Analysis reveals that ferritin and iron regulatory proteins are the main intracellular mechanisms contributing to the mucosal block, findings congruent with experimental predictions. Lastly, DMT1 endocytosis is shown to play a role in limiting total iron uptake by enterocytes but does not contribute to the decrease in total iron transfer across their basal membrane seen in the mucosal block.

Motivation: Proteins, especially those involved in signaling pathways are composed of functional modules connected by linker domains with varying degrees of flexibility. To understand the structure-function relationships in these macromolecules, it is helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways. Results: We introduce a new tool, mol2sphere, that transforms the atomistic structure of a macromolecule into a series of linked spheres corresponding to domains. It does this with a k-means clustering algorithm. It may be used for visualization or for coarse grain modeling and simulation. Availability and implementation: PyMOL plugin, source, and documentation. https://nmrbox.org/registry/mol2sphere. SpringSaLaD executables and documentation: http://vcell.org/ssalad, SpringSaLaD v.2 source: https://github.com/jmasison/SpringSaLaD.