Single-protein Diffusion in the Periplasm of Escherichia coli.

The width of the periplasmic space of Gram-negative bacteria is only about 25-30 nm along the long axis of the cell, which affects free diffusion of (macro)molecules. We have performed single-particle displacement measurements and diffusion simulation studies to determine the impact of confinement on the apparent mobility of proteins in the periplasm of Escherichia coli. The diffusion of a reporter protein and of OsmY, an osmotically regulated periplasmic protein, is characterized by a fast and slow component regardless of the osmotic conditions. The diffusion coefficient of the fast fraction increases upon osmotic upshift, in agreement with a decrease in macromolecular crowding of the periplasm, but the mobility of the slow (immobile) fraction is not affected by the osmotic stress. We observe that the confinement created by the inner and outer membranes results in a lower apparent diffusion coefficient, but this can only partially explain the slow component of diffusion in the particle displacement measurements, suggesting that a fraction of the proteins is hindered in its mobility by large periplasmic structures. Using particle-based simulations, we have determined the confinement effect on the apparent diffusion coefficient of the particles for geometries akin the periplasmic space of Gram-negative bacteria.

Implications of different membrane compartmentalization models in particle-based in silico studies.

Studying membrane dynamics is important to understand the cellular response to environmental stimuli. A decisive spatial characteristic of the plasma membrane is its compartmental structure created by the actin-based membrane-skeleton (fences) and anchored transmembrane proteins (pickets). Particle-based reaction-diffusion simulation of the membrane offers a suitable temporal and spatial resolution to analyse its spatially heterogeneous and stochastic dynamics. Fences have been modelled via hop probabilities, potentials or explicit picket fences. Our study analyses the different approaches' constraints and their impact on simulation results and performance. Each of the methods comes with its own constraints; the picket fences require small timesteps, potential fences might induce a bias in diffusion in crowded systems, and probabilistic fences, in addition to carefully scaling the probability with the timesteps, induce higher computational costs for each propagation step.

A Particle-Based COVID-19 Simulator With Contact Tracing and Testing.

Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.

Rule-Based Modeling Using Wildcards in the Smoldyn Simulator.

Many biological molecules exist in multiple variants, such as proteins with different posttranslational modifications, DNAs with different sequences, and phospholipids with different chain lengths. Representing these variants as distinct species, as most biochemical simulators do, leads to the problem that the number of species, and chemical reactions that interconvert them, typically increase combinatorially with the number of ways that the molecules can vary. This can be alleviated by "rule-based modeling methods," in which software generates the chemical reaction network from relatively simple "rules." This chapter presents a new approach to rule-based modeling. It is based on wildcards that match to species names, much as wildcards can match to file names in computer operating systems. It is much simpler to use than the formal rule-based modeling approaches developed previously but can lead to unintended consequences if not used carefully. This chapter demonstrates rule-based modeling with wildcards through examples for signaling systems, protein complexation, polymerization, nucleic acid sequence copying and mutation, the "SMILES" chemical notation, and others. The method is implemented in Smoldyn, a spatial and stochastic biochemical simulator, for both generate-first and on-the-fly expansion, meaning whether the reaction network is generated before or during the simulation.