Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit.

ABSTRACT: Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies.

SUBMITTER: Moshiri N

PROVIDER: S-EPMC10038133 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit.

Moshiri Niema N

GigaByte (Hong Kong, China) 20220124

Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a da ...[more]

PMID: 36968795

Similar Datasets

Efficient simulation of neural development using shared memory parallelization.

Project description:The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of brain development: morphological growth, migration, and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle has agents called fronts execute model-specific code. In the case of a growing dendritic or axonal front, this will be a choice between extension, branching, or growth termination. Somatic fronts can migrate to new positions and any front can be retracted to prune parts of neurons. Collision detection prevents new or migrating fronts from overlapping with existing ones. NeuroDevSim is a multi-core program that uses an innovative shared memory approach to achieve parallel processing without messaging. We demonstrate linear strong parallel scaling up to 96 cores for large models and have run these successfully on 128 cores. Most of the shared memory parallelism is achieved without memory locking. Instead, cores have only write privileges to private sections of arrays, while being able to read the entire shared array. Memory conflicts are avoided by a coding rule that allows only active fronts to use methods that need writing access. The exception is collision detection, which is needed to avoid the growth of physically overlapping structures. For collision detection, a memory-locking mechanism was necessary to control access to grid points that register the location of nearby fronts. A custom approach using a serialized lock broker was able to manage both read and write locking. NeuroDevSim allows easy modeling of most aspects of neural development for models simulating a few complex or thousands of simple neurons or a mixture of both.Code available athttps://github.com/CNS-OIST/NeuroDevSim.

| S-EPMC10400717 | biostudies-literature

phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.

Project description:Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.

| S-EPMC9094560 | biostudies-literature

Is the network of heterosexual contact in Japan scale free?

Project description:Elucidation of the structure of human sexual networks is not only an interesting topic in the area of social networks but also an important clue for understanding the spreading risk of sexually transmitted infections (STIs). Some previous studies have indicated that sexual networks are scale free, while others have suggested that they are not. We conducted a Web-based survey on sexual contact in Japan to collect data on cumulative (total) heterosexual partners and the number of recent (in the last three or previous three months) heterosexual partners. To determine whether the number of heterosexual contacts in Japan has a power-law tail, we used maximum likelihood fitting methods and Kolmogorov-Smirnov tests. For confirmation, we also used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Our results indicate that the distributions of the number of sexual partners in Japan have power-law tails.

| S-EPMC6711537 | biostudies-literature

A flexible ChIP-sequencing simulation toolkit.

Project description:BackgroundA major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.ResultsWe present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips .ConclusionsChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

| S-EPMC8056602 | biostudies-literature

Large-scale network interactions supporting item-context memory formation.

Project description:Episodic memory is thought to involve functional interactions of large-scale brain networks that dynamically reconfigure depending on task demands. Although the hippocampus and closely related structures have been implicated, little is known regarding how large-scale and distributed networks support different memory formation demands. We investigated patterns of interactions among distributed networks while human individuals formed item-context memories for two stimulus categories. Subjects studied object-scene and object-location associations in different fMRI sessions. Stimulus-responsive brain regions were organized based on their fMRI interconnectivity into networks and modules using probabilistic module-detection algorithms to maximize measurement of individual differences in modular structure. Although there was a great deal of consistency in the modular structure between object-scene and object-location memory formation, there were also significant differences. Interactions among functional modules predicted later memory accuracy, explaining substantial portions of variability in memory formation success. Increased interactivity of modules associated with internal thought and anti-correlation of these modules with those related to stimulus-evoked processing robustly predicted object-scene memory, whereas decreased interactivity of stimulus-evoked processing modules predicted object-location memory. Assessment of individual differences in network organization therefore allowed identification of distinct patterns of functional interactions that robustly predicted memory formation. This highlights large-scale brain network interactions for memory formation and indicates that although networks are largely robust to task demands, reconfiguration nonetheless occurs to support distinct memory formation demands.

| S-EPMC6328164 | biostudies-literature

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code.

Project description:NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

| S-EPMC5487483 | biostudies-other

Contact-FP: A Dimerization-Dependent Fluorescent Protein Toolkit for Visualizing Membrane Contact Site Dynamics.

Project description:Membrane contact sites (MCSs) are sites of close apposition between two organelles used to exchange ions, lipids, and information. Cells respond to changing environmental or developmental conditions by modulating the number, extent, or duration of MCSs. Because of their small size and dynamic nature, tools to study the dynamics of MCSs in live cells have been limited. Dimerization-dependent fluorescent proteins (ddFPs) targeted to organelle membranes are an ideal tool for studying MCS dynamics because they reversibly interact to fluoresce specifically at the interface between two organelles. Here, we build on previous work using ddFPs as sensors to visualize the morphology and dynamics of MCSs. We engineered a suite of ddFPs called Contact-FP that targets ddFP monomers to lipid droplets (LDs), the endoplasmic reticulum (ER), mitochondria, peroxisomes, lysosomes, plasma membrane, caveolae, and the cytoplasm. We show that these probes correctly localize to their target organelles. Using LDs as a test case, we demonstrate that Contact-FP pairs specifically localize to the interface between two target organelles. Titration of LD-mitochondria ddFPs revealed that these sensors can be used at high concentrations to drive MCSs or can be titrated down to minimally perturb and visualize endogenous MCSs. We show that Contact-FP probes can be used to: (1) visualize LD-mitochondria MCS dynamics, (2) observe changes in LD-mitochondria MCS dynamics upon overexpression of PLIN5, a known LD-mitochondrial tether, and (3) visualize two MCSs that share one organelle simultaneously (e.g., LD-mitochondria and LD-ER MCSs). Contact-FP probes can be optimized to visualize MCSs between any pair of organelles represented in the toolkit.

| S-EPMC10846013 | biostudies-literature

FEASST: Free Energy and Advanced Sampling Simulation Toolkit.

Project description: Not available

| S-EPMC7339717 | biostudies-literature

Efficient prediction of human protein-protein interactions at a global scale.

Project description:BackgroundOur knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.ResultsOn the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.ConclusionsThe speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

| S-EPMC4272565 | biostudies-literature

Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation.

Project description:It is chemically intuitive that an optimal atom centered basis set must adapt to its atomic environment, for example by polarizing toward nearby atoms. Adaptive basis sets of small size can be significantly more accurate than traditional atom centered basis sets of the same size. The small size and well conditioned nature of these basis sets leads to large saving in computational cost, in particular in a linear scaling framework. Here, it is shown that machine learning can be used to predict such adaptive basis sets using local geometrical information only. As a result, various properties of standard DFT calculations can be easily obtained at much lower costs, including nuclear gradients. In our approach, a rotationally invariant parametrization of the basis is obtained by employing a potential anchored on neighboring atoms to ultimately construct a rotation matrix that turns a traditional atom centered basis set into a suitable adaptive basis set. The method is demonstrated using MD simulations of liquid water, where it is shown that minimal basis sets yield structural properties in fair agreement with basis set converged results, while reducing the computational cost in the best case by a factor of 200 and the required flops by 4 orders of magnitude. Already a very small training set yields satisfactory results as the variational nature of the method provides robustness.

| S-EPMC6096449 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data