Tools

pyJUSBB

The PyJUSBB are bunch of python-based scripts that give a sample of individuals, identifies associations between summary measures of gray matter volume (extracted from preprocessed (with CAT12 software: http://www.neuro.uni-jena.de/cat/) T1Weighted data) and a broad range of behavioural scores. The tools further provide a ranking of the top correlating scores and assess stability of the outcomes within two independent sample of age and sex-matched individuals from the whole sample. The output is either a Figure or a CSV file.

Modelling and simulation

PyNN

PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian 2) and on a number of neuromorphic hardware systems. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way. Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models.

Network level simulationModelling and simulation

PyramidalExplorer

It consists of a set of functionalities that allow possible regional differences in the pyramidal cell architecture to be interactively discovered by combining quantitative morphological information about the structure of the cell with implemented functional models. The key contribution of this tool is the morpho-functional oriented design that allows the user to navigate within the 3D dataset, filter and perform Content-Based Retrieval operations. As a case study, we present a human pyramidal neuron with over 9000 dendritic spines in its apical and basal dendritic trees. Using PyramidalExplorer, we were able to find unexpected differential morphological attributes of dendritic spines at particular compartments of the neuron, revealing new aspects of the morpho-functional organization of the pyramidal neuron.

Modelling and simulation

RTNeuron

RTNeuron is a scalable real-time rendering tool for the visualisation of neuronal simulations based on cable models. Its main utility is twofold: the interactive visual inspection of structural and functional features of the cortical column model and the generation of high quality movies and images for presentations and publications. The package provides three main components: A high level C++ library. A Python module that wraps the C++ library and provides additional tools. The Python application script rtneuron-app.py A wide variety of scenarios is covered by rtneuron-app.py. In case the user needs a finer control of the rendering, such as in movie production or to speed up the exploration of different data sets, the Python wrapping is the way to go. The Python wrapping can be used through an IPython shell started directly from rtneuron-app.py or importing the module rtneuron into own Python programs. GUI overlays can be created for specific use cases using PyQt and QML.

Data analysis and visualisationModelling and simulation

SBtabVFGEN

Convert a model that has been hand written in the Sbtab format to VFGEN's format, NEURON's MOD file format, and optionally SBML (this is done if libsbml is installed with R bindings). This model conversion tool can be used by scientists working in the field of systems biology and all adjacent fields that work with ordinary differential equation (ODE) models. It can be helpful when collaborating with other researchers as it keeps the model separate from any programming language choice. The user writes the model in SBtab form, a simple, human readable format; afterwards this SBtab model can be converted to an ODE and further processed via vfgen (an alternative to vfgen is being worked on, if needed). The final result is code for the ODE right hand side function and analytical jacobian function (among other things) in the chosen programming language. This tool prepares a model M for use in numerical analysis application such as parameter estimation

Modelling and simulation

Scalasca

Scalasca is a software tool that supports the performance optimization of parallel programs by measuring and analyzing their runtime behavior. The analysis identifies potential performance bottlenecks – in particular those concerning communication and synchronization – and offers guidance in exploring their causes. Scalasca supports the performance optimization of simulation codes on a wide range of current HPC platforms. Its powerful analysis and intuitive result presentation guides the developer through the tuning process. Scalasca targets mainly scientific and engineering applications based on the programming interfaces MPI and OpenMP, including hybrid applications based on a combination of the two. The tool has been specifically designed for use on large-scale systems including IBM Blue Gene and Cray XT, but is also well suited for small- and medium-scale HPC platforms.

Modelling and simulation

Score-P

Score-P is a software system that provides a measurement infrastructure for profiling, event trace recording, and online analysis of High Performance Computing (HPC) applications. It is being developed with the objective of creating a common basis for several complementary optimization tools in the service of enhanced scalability, improved interoperability, and reduced maintenance cost. Currently, it works with the analysis tools Cube, Extra-P, Periscope, Scalasca Trace Tools, Vampir, and Tau and is open for other tools.

Modelling and simulation

SDA

SDA (Simulation of Diffusional Association) is a Brownian dynamics simulation software package for the simulation of the diffusion of biomacromolecules in aqueous solution. SDA can be used to compute bimolecular diffusional association rate constants and to predict the structures of diffusional encounter complexes. It can also be used to simulate dilute or concentrated protein solutions and to investigate the adsorption of proteins to solid surfaces. SDA7 is available for standalone use and a subset of the functionality is implemented in the webSDA webserver.

Modelling and simulationMolecular and subcellular simulation

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

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

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

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

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

Subcellular model building and calibration tool set

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.

Modelling and simulationMolecular and subcellular simulation

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