This tool allows import of SBML model files from the subcellular model building and calibration toolset workflow or other external sources. The tool allows users to setup and configure BioNetGen and STEPS simulations. Users can populate mesh models of spines and other neural structures, and run stochastic simulations of signalling pathways.
The subcellular application was designed as a hub web based environment for creation and simulation of reaction-diffusion models integrated with the molecular repository. It allows also to import, combine and simulate existing models expressed with BNGL and SBML languages. Two types of models are supported: rule-based models convenient and computationally efficient for modeling big protein signaling complexes and chemical reaction network models. The subcellular application is integrated with a number of solvers for reaction-diffusion systems of equations. It supports simulation of spatially distributed systems using STEPS (stochastic engine for pathway simulation) – which provides spatial stochastic and deterministic solvers for simulation of reactions and diffusion on tetrahedral meshes. The application provides as well a number of facilities for visualizing the models geometry and the results of the simulations. The molecular repository is a publicly available database of biological information, relevant for brain molecular network modeling. It accommodates several types of biological information which are not available from existing public databases, such as concentrations of proteins in different subcellular compartments of neuronal and glial cells, kinetic data on protein interactions specific for brain and synaptic signaling and plasticity, data on molecules mobility. The repository is integrated with the subcellular application. They share the same set of entities described by BioNetGen expressions. The molecular repository can be queried from the subcellular application and the results of the query can be added to a molecular network model.
This workflow has been developed to tackle the challenge of building and analyzing biochemical pathway models, combining pre-existing tools and custom-made software. At the root of our implementation is the Sbtab format, a file format that can store biochemical models and associated data in an easily readable and expandable way.
The Synaptic events fitting Notebook implements a Use Case of the Brain Simulation Platform. Starting from any given model description (.mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. The traces and the model are stored in the Knowledge Graph. The user can run the fitting procedure using UNICORE authentication on JURECA or on the NSG, check the job status and download and analyse the results.
This software is developed in a jupyter notebook inside Collaboratory v1 of the HBP. It allows a user to configure and test, through an intuitive GUI, different synaptic plasticity models and protocols on any of the single cell optimized models present in the model catalog. It consists of two tabs: "Config", where the user can specify the plasticity model to use and synaptic parameters such as location, initial weight, activation pattern, additional somatic current injections, or voltage clamp parameter, and "Sim", where the user can define the recording location, weight's evolution, and also the number of simulations to run (to obtain average results). The results are plotted at the end of the simulation and the traces can be downloaded for further analysis.
A fast implementation of The Virtual Brain brain network simulator is written in C using a host of optimizations that make brain simulation faster parallelized (multithreading) containerized (can be conveniently run e.g. through Docker, Shifter or Singularity, without the need to install dependencies or set up environment) uses the Deco-Wang (aka "ReducedWongWang") neural mass model to simulate local brain region activity as described in Deco et al., 2014, Journal of Neuroscience or Schirner et al., 2018, eLife An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888
The Virtual Brain (TVB) is an open-source platform for constructing and simulating personalised brain network models. The TVB-on-EBRAINS ecosystem includes a variety of prepackaged modules, integrated simulation tools, pipelines and data sets for easy and immediate use on EBRAINS. Process your large cohort databases and use these results to develop potential medical treatments, therapies or diagnostic procedures.
Access the TVB GUI from the Internet and simulate brain network models on HCP. TheVirtualBrain is a framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity. TheVirtualBrain uses tractographic data (DTI/DSI) to generate connectivity matrices and build cortical and subcortical brain networks. The connectivity matrix defines the connection strengths and time delays via signal transmission between all network nodes. Various neural mass models are available in the repertoire of TheVirtualBrain and define the dynamics of a network node. Together, the neural mass models at the network nodes and the connectivity matrix define the Virtual Brain. TheVirtualBrain simulates and generates the time courses of various forms of neural activity including Local Field Potentials (LFP) and firing rate, as well as brain imaging data such as EEG, MEG and BOLD activations as observed in fMRI. TheVirtualBrain is foremost a scientific simulation platform and provides all means necessary to generate, manipulate and visualize connectivity and network dynamics. In addition, TheVirtualBrain comprises a set of classical time series analysis tools, structural and functional connectivity analysis tools, as well as parameter exploration facilities. An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888
Intel® Threading Building Blocks (Intel® TBB) is a widely used C++ library for shared memory parallel programming and heterogeneous computing (intra-node distributed memory programming). The library provides a wide range of features for parallel programming that include: Generic parallel algorithms Concurrent containers A scalable memory allocator Work-stealing task scheduler Low-level synchronization primitives Additionally, it fully supports nested parallelism, so you can build larger parallel components from smaller parallel components. To use the library, you specify tasks, not threads, and let the library map tasks onto threads in an efficient manner. It does not require any special compiler support and has ports to multiple architectures that include Intel® architectures and ARM.
A viewer that allows users to view brain atlasses on top of a 3d brain model. The Brain Atlas Viewer allows users to inspect the location and shape of different brain regions and their associated function. Brain regions can be selected by anatomy or by function. Descriptions are available in English, Arabic, Hebrew, and German. Regions are annotated with their function. TVB Brain Atlas Viewer is an interactive software that can be operated via touch screen. It was part of the HBP Travelling Exhibition that started in July 2019 at Bloomfield Museum in Jerusalem organized by the HBP Museum Program (SP11). An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888
TVB-HPC provides a framework for generating high-performance computational kernels which can be run on HPC systems with or without hardware accelerators for large scale parameter fitting of brain models An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888
The TVB pipeline allows neuroscientists to automatically extract structural connectomes from diffusion-weighted MRI data and functional connectomes from fMRI data based on a number of state-of-the-art methods for image processing, tractography reconstruction and connectome generation. Pipeline output can be directly uploaded to The Virtual Brain neuroinformatics platform for large-scale brain simulation. Further pipeline outputs include: raw tractography output (track streamlines), structural (coupling weights and distances) and functional connectomes, region-wise fMRI time series, M/EEG region-wise source activity time series. The pipeline supports the following atlasses: AAL, AAL2, Craddock200, Craddock400, Desikan Killiany, Destrieux, Human Connectome Project Multimodal Parcellation and Perry512. The pipeline is available as a Docker container based on the BIDS MRtrix3 App containing environment and software for connectome extraction (e.g. FreeSurfer, FSL, MRtrix). The container makes use of parallelized software that can be run with multiple threads locally or on supercomputers. Input data must be provided in BIDS format. As a minimum, dwMRI and strucutral MRI scans need to be provided. In addition, the pipeline can process fMRI (region-wise fMRI time courses and functional connectomes), EEG and MEG data (region-wise source activity time courses).
UG4 (Unstructured Grids 4) is an extensive, flexible, cross-platform open source simulation framework for the numerical solution of systems of partial differential equations. Using Finite Element and Finite Volume methods on hybrid, adaptive, unstructured multigrid hierarchies, UG4 allows for the simulation of complex real world models (physical, biological etc.) on massively parallel computer architectures. UG4 is implemented in the C++ programming language and provides grid management, discretization and (linear as well as non-linear) solver utilities. It is extensible and customizable via its plugin mechanism. The highly scalable MPI based parallelization of UG4 has been shown to scale to hundred thousands of cores. Simulation workflows are defined either using the Lua scripting language or the graphical VRL interface https://vrl-studio.mihosoft.eu/. Besides that, UG4 can be used as a library for third-party code. Several examples are provided in the Examples application that can be used for simulations of the corresponding phenomena but also serve as demonstration modules for implementing user-defined plugins and scripts. By developing custom plugins, users can extend the functionality of the framework for their particular purposes. The framework provides coupling facilities for the models implemented in different plugins. Key elements of UG4 are: Efficient solvers on distributed, adaptive multigrid hierarchies. A flexible component based discretization system. Efficient support for massively parallel computer architectures. Full scripting support. A modular plugin based architecture.
Uncertainty quantification via ABC-MCMC with copulas as well as global sensitivity analysis for ODE models in systems biology. This R package can approximate the posterior probability density of Parameters for Ordinary Differential Equation models. The ABC sampler used here is developed to be fairly model agnostic, but the supplied tool set and R functions specifically target ODEs as they are fast enough to simulate to permit Bayesian methods. Bayesian methods for parameter estimation are resource intensive and therefore require some consideration of efficiency in simulation. Other modeling frameworks exist, with benefits of higher accuracy in specific scenarios (e.g. low molecule count), or reduced complexity (rule based models). We have written a sibling library for R that facilitates the simulation of systems biology specific models using the GNU scientific library solvers (and models written in C). With powerful enough computing hardware, or small enough models, these frameworks can be combined with this package. We write models using the SBtab format and automatically generate C-code as well as R-code for them, the R-code can be used with deSolve (an R package) while the C-code is compatible with gsl_odeiv2 solvers. Code generation is done via SBtabVFGEN (an R package) and vfgen (a standalone software). In addition, we are writing our own substitution for vfgen, to avoid single points of failure. But the model setup phase can be completely sidestepped by writing the C-code manually (or generating it in any other way).
ViSimpl involves two components: SimPart and StackViz. SimPart is a three-dimensional visualizer for spatio-temporal data that allow spatio/temporal analysis of the simulation data, using particle-based rendering. StackViz illustrates how the electrophysiological variables evolve over time and provides a temporal representation of the data at different aggregation levels. They allow users to visually discriminate the activity of different groups of neurons, and provide detailed information about individual neurons of interest. These components share synchroniszed playback control of the simulation being analyzsed and work together as linked views, although they are loosely coupled and can also be used independently. They are ready to be used with BlueConfig Datasets among other file formats such as specific HDF5 and CSV. VisSimpl can be coupled with NeuroScheme for adding functionality such as navigate through the underlying structure of the data using symbolic representations and different levels of abstraction.
These Python notebooks reproduce some figures in the following preprint using the libraries pyMOU and NetDynFlow: https://www.biorxiv.org/content/10.1101/531830v2 The notebook 1_MOUEC_Estimation.ipynb should be executed first to tune the model to the fMRI data. The other notebooks can be used for classification and interpretation of the model (using the flow for network analysis). The data files are: BOLD time series in ts_emp.npy structural connectivity in SC_anat.npy ROI labels in ROI_labels.npy --- ####Notebook 1_MOUEC_Estimation.ipynb This notebook calculates the functional connectivity and the model-based effective connectivity for each session (or run) and subject from the BOLD time series. The model is a multivariate Ornstein-Uhlenbeck (MOU) process, whose estimation procedure is implemented in the pyMOU library. The model estimates and other measures are stored in the form of arrays in the model_param_movie folder. --- ####Notebooks 2a_ClassificationTasks.ipynb and 2b_ClassificationSubjects.ipynb These notebooks compare the performances of the several type of connectivity measures (including functional and effective connectivity) in identifying cognitive tasks and subjects. They rely on the scikit.learn library. --- ####Notebook 3a_Flow.ipynb This notebook uses the NetDynFlow library to calculate the flow, which is network-oriented analysis of the MOU model fitted to the BOLD data. The flow corresponds to the input response of the network to perturbation (or stimulation of given regions). The flow captures network effects that arise from the recurrent connectivity, i.e. also taking into account indirect paths between all pairs of regions. --- ####Notebook 3b_Communities.ipynb This notebook detects communities based on the flow, namely brain regions are grouped together if they exchange strong flow in the network. It also compares the community structure between rest and movie.
The τRAMD (τ-Random Acceleration Molecular Dynamics) technique makes use of RAMD simulations to compute relative residence times (or dissociation rates) of protein-ligand complexes. In the RAMD method, the egress of a small molecule from a target receptor is accelerated by the application of an adaptive randomly oriented force on the ligand. This enables ligand egress events to be observed in short, nanosecond timescale simulations without imposing any bias regarding the ligand egress route taken. Apart from the estimation of relative residence times, the τRAMD method can be used to investigate dissociation mechanisms and characterize transition states by analysing the RAMD trajectories with the MD-IFP (Molecular Dynamics - Interaction Fingerprint) tool. The combined use of τRAMD and MD-IFP may assist the early stages of drug discovery campaigns for the design of new molecules or ligand optimization.
τRAMD is a computationally efficient procedure that enables the computation of relative residence times (τ) or dissociation rates of protein-ligand complexes. It makes use of random acceleration molecular dynamics (RAMD) simulations to facilitate ligand egress in a short timescale and without imposing any bias regarding the ligand egress route. τRAMD is a powerful tool for ranking drug candidates according to their residence times and it can be used with the MD-IFP (Molecular Dynamics - Interaction Fingerprint) tool to investigate dissociation mechanisms and pathways. The combined use of τRAMD and MD-IFP may assist the design of new molecules or ligand optimization.
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