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MesoRD
A tool for simulating stochastic reaction-diffusion systems as modeled by the reaction diffusion master equation. The simulated systems are defined in the Systems Biology Markup Language with additions to define compartment geometries. MesoRD 1.0 supports scale-dependent reaction rate constants and reactions between reactants in neighbouring subvolumes. These new features make it possible to construct physically consistent models of diffusion-controlled reactions also at fine spatial discretization.
IMP / Integrative Modeling Platform
A package which facilitates the writing of integrative modeling applications, the development of new model representations, scoring functions, sampling schemes, and analysis methods, and the distribution of integrative modeling applications. IMP's broad goal is to contribute to a comprehensive structural characterization of biomolecules ranging in size and complexity from small peptides to large macromolecular assemblies. This tool implements an integrative approach to structural and dynamics problems.
SPSens
Provides methods for parameter sensitivity analysis of discrete stochastic biochemical reaction networks. SPSens allows users to switch between algorithms to find the most efficient method for their particular problem. The software also includes exact Gillespie's stochastic simulation algorithm (SSA) that can be used to generate individual sample paths or ensembles of trajectories using the identical model definition, as well as the option to perform sensitivity analysis or simulation using antithetic sampling (AS).
MEANS / Moment Expansion Approximation iNference and Simulation
Implements an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. MEANS enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. MEANS is complemented with an implementation of exact stochastic simulation, as well as support for different ODE solvers and sensitivity analysis, a parameter estimation tool and specific functions for the visualization of results.
SMeagol
Generates highly realistic single molecule microscopy time-lapse image series aimed primarily at single particle tracking applications. The purpose of SMeagol is to enable realistic comparisons between the output of advanced analysis methods and known ground truth. SMeagol includes an extended MesoRD version for simulation of 3D diffusion in cellular compartments, diffusion limited reaction kinetics, surface adsorption, reactions in membranes and other complex aspects of reaction diffusion kinetics that do occur in cells, but are not considered in SPT analysis algorithms. In addition to the molecules’ trajectories, SMeagol integrates the 3D point spread function of the microscope, the kinetics of photo-activation, blinking and bleaching of the simulated fluorophores, background noise and camera specific parameters.
SELANSI / SEmi-LAgrangian SImulation of GRNs
Approximates the chemical master equation (CME) by the partial integral differential equation (PIDE) model. SELANSI is a toolbox for simulation of stochastic multidimensional gene regulatory networks. It computes numerical solution by an efficient and scalable semilagrangian method, providing the temporal evolution of the protein probability density function. User can specify the size and topology of the network, the kinetics, parameter values, time horizon and discretization levels for simulation.
SpringSaLaD / Springs, Sites, and Langevin Dynamics
New
Enables spatial, stochastic, particle-based modeling of biochemical systems. SpringSaLaD is a standalone modeling and simulation package that implements spatial, particle-based models with excluded volume and accurate biochemical reactions, including the treatment of allostery. The software provides several convenience methods for constructing large linear polymers, and other convenience, methods for constructing large arrays of linked receptors. It comes packaged with an interactive three-dimensional viewer to visualize simulation results.
PISKaS / Parallel Implementation of a Spatial Kappa Simulator
Determines simulation of Complex Systems (CSs) based on the stochastic simulation paradigm. PISKaS is a multiscale simulation tool suitable to perform stochastic simulations on distributed memory computing architectures. It expands the Kappa language by allowing the explicit declaration of compartments interconnected by links to simulate heterogeneous environments. It can also be used to model systems unrelated to chemistry.
LASSIE / LArge-Scale SImulator
Simulates large-scale reaction-based models of cellular processes. LASSIE automatically converts reaction-based models of biological systems into the corresponding systems of Ordinary Differential Equations (ODEs). It is based on a Runge-Kutta-Fehlberg (RKF) method, which is a single-step explicit algorithm with variable step-size, and the Backward Euler method. The tool permits to make simulation of even larger models, taking just an average less than 15 seconds to simulate models.
STEPS / STochastic Engine for Pathway Simulation
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.
URDME
A modular software framework for spatial stochastic simulation. The goal of URDME is twofold: firstly, it provides applied users with a powerful and user-friendly modeling environment that supports realistic geometries. Secondly, URDME facilitates the development of new computational methods by taking care of the technical details concerning the geometry, the mesh generation, and the assembly of local rate constants. By providing a well-defined interface to the modeling environment, new algorithms can be incorporated into the URDME framework as plug-in solvers.
GridCell
A three-dimensional simulation environment for investigating the behaviour of biochemical networks under a variety of spatial influences including crowding, recruitment and localization. GridCell enables the tracking and characterization of individual particles, leading to insights on the behaviour of low copy number molecules participating in signaling networks. The simulation space is divided into a discrete 3D grid that provides ideal support for particle collisions without distance calculation and particle search. SBML support enables existing networks to be simulated and visualized. The user interface provides intuitive navigation that facilitates insights into species behaviour across spatial and temporal dimensions. We demonstrate the effect of crowing on a Michaelis-Menten system.
ProMesh
Provides support for unstructured grids of 1, 2, and 3 dimensions. ProMesh is a flexible cross-platform meshing software which features a broad range of modular tools to generate, remesh, transform, and optimize computational grids. The software includes tools to perform quality remeshing of triangular grids, both in two or three dimensions. It natively supports hybrid element meshes consisting of triangles, quadrilaterals, tetrahedra, pyramids, prisms, hexahedra, and octahedra.
BioNetS / BlOchemical NETwork Stochastic Simulator
Obsolete
Conducts full discrete simulations. BioNetS is based on an implementation of the Gillespie algorithm. It can resolve the chemical Langevin equations and handle hybrid models in which chemical species, that are present in low abundances, are treated discretely. This tool can be useful in the study of the stochastic dynamics of large chemical networks. It allows users to impart the chemical species in the network should be treated as discrete random variables.
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