All Tools

The SSB Toolkit is a Python library specifically designed for conducting simulations of mathematical models that represent the signal-transduction pathways of G-protein coupled receptors (GPCRs). This library consists of a set of systems biology simulation routines, enabling the investigation of pharmacodynamic models associated with GPCRs. It provides a means to explore how the structural characteristics of these receptors influence subcellular signaling dynamics.

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in arbitrarily complex 3D geometries. Our core simulation algorithm is an implementation of Gillespie's SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh. While it was mainly developed for simulating detailed models of neuronal signaling pathways in dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role. STEPS also supports accurate and efficient computational of local membrane potentials on tetrahedral meshes, with the addition of voltage-gated channels and currents. Tight integration between the reaction-diffusion calculations and the tetrahedral mesh potentials allows detailed coupling between molecular activity and local electrical excitability. We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analyzing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution. STEPS 3.0.0 and above provide early parallel solution for stochastic spatial reaction-diffusion and electric field simulation. STEPS 3.6.0 and above provide a new set of APIs (API2) to speedup STEPS model development. Models developed with the old API (API1) are still supported.

The toolset includes interoperable modules for: model building, calibration (parameter estimation) and model analysis. All information needed to perform these tasks are stored in a structured, human- and machine-readable file format based on SBtab. This information includes: models, experimental calibration data and prior assumptions on parameter distributions. The toolset enables simulations of the same model in simulators with different characteristics, e.g. STEPS, NEURON, MATLAB’s Simbiology and R via automatic code generation.

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.

Surf Ice is a tool for surface rendering the cortex with overlays to illustrate tractography, network connections, anatomical atlases and statistical maps. While there are many alternatives, Surf Ice is easy to use and uses advances shaders to generate stunning images. It supports many popular mesh formats [3ds, ac3d, BrainVoyager (srf), ctm, Collada (dae), dfs, dxf, FreeSurfer (Asc, Srf, Curv, gcs, Pial, W), GIfTI (gii), gts, lwo, ms3d, mz3, nv, obj, off, ply, stl, vtk], connectome formats (edge/node) and tractography formats [bfloat, pdb, tck, trk, vtk]. Surf Ice uses three stages to draw your image. The first two stages are computed in 3D and create both an image (left column) and a depth buffer (right column). The first stage draws all the items, while the second stage omits the background anatomical image. The final stage uses the 2D outputs of the prior stages. The depth map from the first stage is used to estimate ambient occlusion (SSAO), and the difference between the depth maps from the previous stages allows the software to infer the depth of the overlays behind the background (depth). The SSAO and depth images are composited with the images from the first two stages to generate the final image.

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.

This repository contains the code to run the model-free and model-based approaches as well as some inputs using the Schaefer 1000 7 RSNs parcellation. The repository is organized in six folders: (1) model-free – whole-brain turbulent-like dynamics; (2) model-based – Hopf whole-brain model with intact structural connectivity; (3) model-based-lesions – Hopf model after simulated attacks; (4) CONN_preprocessing – CONN toolbox denoising outputs; (5) time-series – inputs for model-free/model-based analyses; (6) lesion-masks – code for the simulated attack approach.

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

Jupyter notebook is a language-agnostic HTML notebook application for Project Jupyter. In 2015, Jupyter notebook was released as a part of The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic release containing both language-agnostic code, such as the IPython notebook, and language specific code, such as the IPython kernel for Python. As computing spans across many languages, Project Jupyter will continue to develop the language-agnostic Jupyter notebook in this repo and with the help of the community develop language specific kernels which are found in their own discrete repos.

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.

Tide (Tiled Interactive Display Environment) is a distributed application that can run on multiple machines to power display walls or projection systems of any size. Its user interface is designed to offer an intuitive experience on touch walls. It works just as well on non touch-capable installations by using its web interface from any web browser. Tide helps users with: Presenting and collaborating on a variety of media such as high-resolution images, movies and pdfs. Sharing multiple desktop or laptop screens using the DesktopStreamer application. Sketching new ideas by drawing on a whiteboard and browsing websites. Interacting with content streamed from remote sources such as high-performance visualisation machines through the Deflect protocol. In particular all Equalizer-based applications as well as Brayns ray-tracing engine have built-in support. Viewing high-resolution, immersive stereo 3D streams on compatible hardware.

Module treem provides data structure and command-line tools for accessing and manipulating the digital reconstructions of the neuron morphology in Stockley-Wheal-Cannon format (SWC).

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

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).

This is an EBRAINS Lab extension providing a file browser for the Data-Proxy straight into Jupyter Lab while bringing the power of drag & drop to manage files from and into local storage, EBRAINS Drive or Data-Proxy (Bucket).