Development of a New Bead Movement-Based Computational Framework Shows that Bacterial Amyloid Curli Reduces Bead Mobility in Biofilms.

Biofilms exist in complex environments, including the intestinal tract, as a part of the gastrointestinal microbiota. The interaction of planktonic bacteria with biofilms can be influenced by material properties of the biofilm. During previous confocal studies, we observed that amyloid curli-containing Salmonella enterica serotype Typhimurium and Escherichia coli biofilms appeared rigid. In these studies, Enterococcus faecalis, which lacks curli-like protein, showed more fluid movement. To better characterize the material properties of the biofilms, a four-dimensional (4D) model was designed to track the movement of 1-µm glyoxylate beads in 10- to 20-µm-thick biofilms over approximately 20 min using laser-scanning confocal microscopy. Software was developed to analyze the bead trajectories, the amount of time they could be followed (trajectory life span), the velocity of movement, the surface area covered (bounding boxes), and cellular density around each bead. Bead movement was found to be predominantly Brownian motion. Curli-containing biofilms had very little bead movement throughout the low- and high-density regions of the biofilm compared to E. faecalis and isogenic curli mutants. Curli-containing biofilms tended to have more stable bead interactions (longer trajectory life spans) than biofilms lacking curli. In biofilms lacking curli, neither the velocity of bead movement nor the bounding box volume was strictly dependent on cell density, suggesting that other material properties of the biofilms were influencing the movement of the beads and flexibility of the material. Taken together, these studies present a 4D method to analyze bead movement over time in a 3D biofilm and suggest curli confers rigidity to the extracellular matrix of biofilms.IMPORTANCE Mathematical models are necessary to understand how the material composition of biofilms can influence their physical properties. Here, we developed a 4D computational toolchain for the analysis of bead trajectories, which laid the groundwork for establishing critical parameters for mathematical models of particle movement in biofilms. Using this open-source trajectory analyzer, we determined that the presence of bacterial amyloid curli changes the material properties of a biofilm, making the biofilm matrix rigid. This software is a powerful tool to analyze treatment- and environment-induced changes in biofilm structure and cell movement in biofilms. The open-source analyzer is fully adaptable and extendable in a modular fashion using VRL-Studio to further enhance and extend its functions.

NeuroBox: Computational Mathematics in Multiscale Neuroscience.

The brain is a complex organ operating on multiple scales. From molecular events that inform electrical and biochemical cellular responses, the brain interconnects processes all the way up to the massive network size of billions of brain cells. This strongly coupled, nonlinear, system has been subject to research that has turned increasingly multidisciplinary. The seminal work of Hodgkin and Huxley in the 1950s made use of experimental data to derive a coherent physical model of electrical signaling in neurons, which can be solved using mathematical and computational methods, thus bringing together neuroscience, physics, mathematics, and computer science. Over the last decades numerous projects have been dedicated to modeling and simulation of specific parts of molecular dynamics, neuronal signaling, and neural network behavior. Simulators have been developed around a specific objective and scale, in order to cope with the underlying computational complexity. Often times a dimension reduction approach allows larger scale simulations, this however has the inherent drawback of losing insight into structure-function interplay at the cellular level. This paper gives an overview of the project NeuroBox that has the objective of integrating multiple brain scales and associated physical models into one unified framework. NeuroBox hosts geometry and anatomical reconstruction methods, such that detailed three-dimensional domains can be integrated into numerical simulations of models based on partial differential equations. The project further focusses on deriving numerical methods for handling complex computational domains, and to couple multiple spatial dimensions. The latter allows the user to specify in which parts of the biological problem high-dimensional representations are necessary and where low-dimensional approximations are acceptable. NeuroBox offers workflow user interfaces that are automatically generated with VRL-Studio and can be controlled by non-experts. The project further uses uG4 as the numerical backend, and therefore accesses highly advanced discretization methods as well as hierarchical and scalable numerical solvers for very large neurobiological problems.

Employing NeuGen 2.0 to automatically generate realistic morphologies of hippocampal neurons and neural networks in 3D.

Detailed cell and network morphologies are becoming increasingly important in Computational Neuroscience. Great efforts have been undertaken to systematically record and store the anatomical data of cells. This effort is visible in databases, such as NeuroMorpho.org. In order to make use of these fast growing data within computational models of networks, it is vital to include detailed data of morphologies when generating those cell and network geometries. For this purpose we developed the Neuron Network Generator NeuGen 2.0, that is designed to include known and published anatomical data of cells and to automatically generate large networks of neurons. It offers export functionality to classic simulators, such as the NEURON Simulator by Hines and Carnevale (2003). NeuGen 2.0 is designed in a modular way, so any new and available data can be included into NeuGen 2.0. Also, new brain areas and cell types can be defined with the possibility of constructing user-defined cell types and networks. Therefore, NeuGen 2.0 is a software package that grows with each new piece of anatomical data, which subsequently will continue to increase the morphological detail of automatically generated networks. In this paper we introduce NeuGen 2.0 and apply its functionalities to the CA1 hippocampus. Runtime and memory benchmarks show that NeuGen 2.0 is applicable to generating very large networks, with high morphological detail.

Mesangium in rats after E. coli neurotoxin shock: a morphometric and light microscopic analysis.

The renal corpuscles of 28 specific-pathogen-free Wistar rats fixed by perfusion were examined light microscopically and morphometrically following experimental protracted neurotoxin shock. The mean diameter of the center sections of 420 renal corpuscles was 113.0 micrometers. The mesangial portion occupied 9.5% of the total area of the renal corpuscle in control rats and increased to a maximum of 17.25% in experimental rats. The number of mesangial nuclei per renal corpuscle in rats with protracted shock showed that expansion of the mesangium with compression of capillary loops was not caused solely by an increase in the number of cells. Furthermore, there was an enlargement of the mesangial cytoplasm and matrix. Activation and proliferation of connective tissue in rats with protracted shock could be observed in the interstitium of various organs as well as in the mesangium of the glomerulus.