Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.Comment: 31 pages, 10 figure

Spores of Clostridium difficile Clinical Isolates Display a Diverse Germination Response to Bile Salts

Clostridium difficile spores play a pivotal role in the transmission of infectious diarrhoea, but in order to cause disease spores must complete germination and return to vegetative cell growth. While the mechanisms of spore germination are well understood in Bacillus, knowledge of C. difficile germination remains limited. Previous studies have shown that bile salts and amino acids play an important role in regulating the germination response of C. difficile spores. Taurocholate, in combination with glycine, can stimulate germination, whereas chenodeoxycholate has been shown to inhibit spore germination in a C. difficile clinical isolate. Our recent studies of C. difficile sporulation characteristics have since pointed to substantial diversity among different clinical isolates. Consequently, in this study we investigated how the germination characteristics of different C. difficile isolates vary in response to bile salts. By analysing 29 isolates, including 16 belonging to the BI/NAP1/027 type, we show that considerable diversity exists in both the rate and extent of C. difficile germination in response to rich medium containing both taurocholate and glycine. Strikingly, we also show that although a potent inhibitor of germination for some isolates, chenodeoxycholate does not inhibit the germination, or outgrowth, of all C. difficile strains. Finally, we provide evidence that components of rich media may induce the germination of C. difficile spores, even in the absence of taurocholate. Taken together, these data suggest that the mechanisms of C. difficile spore germination in response to bile salts are complex and require further study. Furthermore, we stress the importance of studying multiple isolates in the future when analysing the nutrients or chemicals that either stimulate or inhibit C. difficile spore germination

A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number

We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steadystate growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity

Crowding Alone Cannot Account for Cosolute Effect on Amyloid Aggregation

Amyloid fiber formation is a specific form of protein aggregation, often resulting from the misfolding of native proteins. Aimed at modeling the crowded environment of the cell, recent experiments showed a reduction in fibrillation halftimes for amyloid-forming peptides in the presence of cosolutes that are preferentially excluded from proteins and peptides. The effect of excluded cosolutes has previously been attributed to the large volume excluded by such inert cellular solutes, sometimes termed “macromolecular crowding”. Here, we studied a model peptide that can fold to a stable monomeric β-hairpin conformation, but under certain solution conditions aggregates in the form of amyloid fibrils. Using Circular Dichroism spectroscopy (CD), we found that, in the presence of polyols and polyethylene glycols acting as excluded cosolutes, the monomeric β-hairpin conformation was stabilized with respect to the unfolded state. Stabilization free energy was linear with cosolute concentration, and grew with molecular volume, as would also be predicted by crowding models. After initiating the aggregation process with a pH jump, fibrillation in the presence and absence of cosolutes was followed by ThT fluorescence, transmission electron microscopy, and CD spectroscopy. Polyols (glycerol and sorbitol) increased the lag time for fibril formation and elevated the amount of aggregated peptide at equilibrium, in a cosolute size and concentration dependent manner. However, fibrillation rates remained almost unaffected by a wide range of molecular weights of soluble polyethylene glycols. Our results highlight the importance of other forces beyond the excluded volume interactions responsible for crowding that may contribute to the cosolute effects acting on amyloid formation

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Movement and habitat use of the snapping turtle in an urban landscape

In order to effectively manage urban habitats, it is important to incorporate the spatial ecology and habitat use of the species utilizing them. Our previous studies have shown that the distribution of upland habitats surrounding a highly urbanized wetland habitat, the Central Canal (Indianapolis, IN, USA) influences the distribution of map turtles (Graptemys geographica) and red-eared sliders (Trachemys scripta) during both the active season and hibernation. In this study we detail the movements and habitat use of another prominent member of the Central Canal turtle assemblage, the common snapping turtle, Chelydra serpentina. We find the same major upland habitat associations for C. serpentina as for G. geographica and T. scripta, despite major differences in their activity (e.g., C. serpentina do not regularly engage in aerial basking). These results reinforce the importance of recognizing the connection between aquatic and surrounding terrestrial habitats, especially in urban ecosystems

Effects of a 1-Week Inpatient Course Including Information, Physical Activity, and Group Sessions for Prostate Cancer Patients

This study aims to explore the effects of a 1-week inpatient course including information, physical activity (PA), and group sessions on physical and mental health-related outcomes for prostate cancer (PCa) patients. Further to assess the patients’ satisfaction with the course. PCa patients completed a questionnaire assessing PA, fatigue, mental distress, and quality of life 1 month before (T0) and 3 months after (T1) the course. Total fatigue, physical fatigue, and PSA anxiety decreased significantly from T0 to T1. No significant changes were observed in the other measures. The majority of the participants were satisfied with the course. In spite of minor reductions in fatigue and PSA anxiety and satisfied patients, the findings indicate that a 1-week inpatient course does not influence substantially on most of the health-related outcomes in PCa patients 3 months after the course