Tools

SDA: Simulation of Diffusional Association

SDA7 can be used to carry out Brownian dynamics simulations of the diffusional association in a continuum aqueous solvent of two solute molecules, e.g. proteins, or of a solute molecule to an inorganic surface. SDA7 can also be used to simulate the diffusion of multiple proteins, in dilute or concentrated solutions, e.g., to study the effects of macromolecular crowding. If the 3D structure of the bound complex is unknown, SDA can be used for rigid-body docking to predict the structure of the diffusional encounter complex or the orientation in which a protein binds to a surface. The configurations obtained from SDA can subsequently be refined by running molecular dynamics simulations to obtain structures for fully bound complexes. If the 3D structure of the bound complex is known, SDA can be used to calculate bimolecular association rate constants. It can also be used to record Brownian dynamics trajectories or encounter complexes and to calculate bimolecular electron transfer rate constants. While these Brownian dynamics simulations are usually carried out with rigid solutes, in SDA7 we give a possibility to assign more than one conformation to each solute molecule. This allows some large-scale internal dynamics of macromolecules to be considered in the simulations. In this SDA distribution, there is a single executable, sda_flex, which will execute different types of simulation: Compute the bimolecular diffusional association rate constant for 2 solutes using a user-defined set of intermolecular contact distances as reaction criteria Compute the rate constants for electron transfer from the relative diffusion of two proteins Perform rigid-body docking of two macromolecules Perform rigid-body docking of a solute and a surface Calculate the time during which user-defined contacts are maintained; this gives an approximation for the lifetimes of a complex. The starting configurations may be from a crystal structure or recorded from a simulation Re-calculate energies for a recorded set of configurations Compute PMFs for protein/surface binding Perform simulations of the diffusion of multiple proteins The simulations can be run in serial or in parallel mode on a shared-memory computer architecture.

Modelling and simulation

siibra-api

siibra-api is an HTTP API for querying and retrieving contents of EBRAINS atlases. Originally built as a backend service for the interactive atlas viewer siibra-explorer, the API has been documented for connecting the brain atlases to other applications and web services.

Brain atlases

siibra-explorer

siibra-explorer is built around an interactive 3D view of the brain displaying a unique selection of detailed templates and parcellation maps for the human, macaque, rat or mouse brain, including BigBrain as a microscopic resolution human brain model at its full resolution of 20 micrometres.

Data analysis and visualisationBrain atlases

siibra-python

The Python library siibra-python is designed for integrating EBRAINS atlases into scripts and computational workflows. Besides providing programmatic access to all functionalities available in the interactive viewer siibra-explorer, it enables more advanced utilization of atlas information.

Brain atlases

siibra-python

siibra-python is a comprehensive Python client providing access to EBRAINS atlases and offering an easy and well-structured way to include maps, reference templates, region definitions and linked datasets in reproducible programmatic workflows.

Single Cell Model Builder Notebook

The current version of the Single Cell Model Builder Notebook implements a Use Case of the Brain Simulation Platform. It allows to select among self-consistent configuration files from previous optimizations. The user may choose and visualize an existing morphology from HBP data, choose a self-consistent set of configuration files for the chosen morphology, visualize the electrophysiological features that will be used as reference by the optimization process, visualize and change the parameters of an existing optimization, configure the BluePyOpt optimization algorithm and run the optimization procedure on CSCS and NSG systems. The Use Case allows the user also to choose either a previous optimization from a CSCS container; or choose the result of his/her own optimization from the Collab storage, and then run and save an analysis of the results.

Modelling and simulation

Single Cell Model Rebuilder Notebook

The current version of the Single Cell Model ReBuilder Notebook implements a Use Case of the Brain Simulation Platform. It allows to select models obtained in previous optimizations. The user may visualize the electrophysiological features for the chosen model, that will be used as reference by the optimization process, visualize and change parameters of an existing optimization, configure the BluePyOpt optimization algorithm and run the optimization procedure on CSCS and NSG systems. The Use Case allows the user also to choose either a previous optimization from a CSCS container; or choose the result of his/her own optimization from the Collab storage, and then run and save an analysis of the results.

Modelling and simulation

SLURM plugin for the co-allocation of compute and data resources

Using the Job Submit Plugin API of the Slurm Workload Manager this plugin is intended for use in a multi-tiered storage cluster. Having considered two storage tiers, called low performance storage (lps) and high performance storage (hps), this plugin allows for the co-allocation of compute and data resources by passing the job storage requirements of jobs individually.

Data

SNT

SNT is ImageJ's framework for semi-automated tracing, visualization, quantitative analyses and modeling of neuronal morphology distributed with Fiji. SNT supports modern multi-dimensional microscopy data, features advanced visualization and quantification tools, and interacts with all major morphology databases. All aspects of the program can be controlled from a user-friendly interface or programmatically, using several of Fiji's supported scripting languages.

Data analysis and visualisationModelling and simulation

Snudda

Snudda is a tool that allows the user to place neurons within multiple volumes, then performs touch detection to infer where putative synapses are based on reconstructed neuron morphologies. To match experimental pair-wise recordings the putative synapses are then pruned to get the final set of synapses. Using neuron models optimised with BluePyOpt the entire network can be simulated using the NEURON simulator.

Modelling and simulationCellular level simulation

Spalloc

The SpiNNaker Allocation Service (Spalloc) allows the board of the SpiNNaker 1Million machine to be split up into user allocations of groups of a number of connected boards. These boards are isolated from the rest of the machine by the service, ensuring that one user’s simulation does not interfere with another. The service allows users to log in using EBRAINS usernames and passwords to see and control their existing allocations. The service also provides a proxy connection to the boards using websockets, which allows the allocation to be used from anywhere in the world. This is used by the SpiNNaker software to enable access to the machine from the EBRAINS Jupyter lab.

SpectralSegmentation

Spectralsegmentation is a pipeline that can be used to detect active neurons and dendrites in ca-imageing data. A series of steps are defined to achieve this. After image stabilization and transposing an image sequence to the (time x pixel) space, Cross-spectral analysis is applied to low frequency(<1Hz) decimated pixel traces. This results in images representing the cross spectral power of each pixel with it's surrounding pixels at increasing frequency components (0.017Hz steps - 0.4Hz). These images are used to define preliminary ROIs using morphological criteria. The ROIs are then constrained to contain only pixels with possitive correlations. The pipeline includes a graphical user interface to edit the automatically extracted ROIs, to add new ones or delete ROIs by further constraining their properties.

Data analysis and visualisation

Spike-based Sampling

Helper library for spike-based sampling in PyNN-supported neural simulators. Spike-based-sampling, sbs, is a software suite that takes care of calibrating spiking neurons for given target distributions and allows the evaluation of these distributions as they are produced by stochastic spiking networks.

SpiNNaker

Simulate or emulate spiking neural networks on SpiNNaker. Models and simulation experiments can be described in a Python script using the PyNN API and submitted either through the EBRAINS Collaboratory website or via our web API (python client available). Results can be viewed via browser and downloaded as data files for analysis, making use e.g. of the data analysis capabilities EBRAINS offers. For real time SpiNNaker simulations, direct use in a neurorobotics simulated environment is also possible.

Neuromorphic computingModelling and simulation

SpykeViewer

It is based on the Neo library, which enables it to load a wide variety of data formats used in electrophysiology. At its core, Spyke Viewer includes functionality for navigating Neo object hierarchies and performing operations on them. A central design goal of Spyke Viewer is flexibility. For this purpose, it includes an embedded Python console for exploratory analysis, a filtering system, and a plugin system. Filters are used to semantically define data subsets of interest. Spyke Viewer comes with a variety of plugins implementing common neuroscientific plots (e.g. rasterplot, peristimulus time histogram, correlogram, and signal plot). Custom plugins for other analyses or plots can be easily created and modified using the integrated Python editor or external editors. Users can download and share additional plugins and other extensions at the Spyke Repository. Among the extensions hosted at the site are plugins for spike detection and spike sorting.

Data analysis and visualisation

sPyNNaker

sPyNNaker is a software package for simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic platform. Operations underpinning realtime SNN execution are presented, including an event-based operating system facilitating efficient time-driven neuron state updates and pipelined event-driven spike processing. Preprocessing, realtime execution, and neuron/synapse model implementations are discussed, all in the context of a simple example SNN. Simulation results are demonstrated, together with performance profiling providing insights into how software interacts with the underlying hardware to achieve realtime execution. System performance is shown to be within a factor of 2 of the original design target of 10,000 synaptic events per millisecond, however SNN topology is shown to influence performance considerably. A cost model is therefore developed characterizing the effect of network connectivity and SNN partitioning. This model enables users to estimate SNN simulation performance, allows the SpiNNaker team to make predictions on the impact of performance improvements, and helps demonstrate the continued potential of the SpiNNaker neuromorphic hardware.

Modelling and simulation

SSB Toolkit

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.

Modelling and simulationMolecular and subcellular simulation

STEPS

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.

Modelling and simulation

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