A large scale monte carlo simulator for cellular microphysiology

Balls, Gregory T.; Baden, Scott B.; Kispersky, Tilman J.; Bartol, Thomas M.; Sejnowski, Terrence J.

doi:10.1109/ipdps.2004.1302957

Cited by 11 publications

(11 citation statements)

References 24 publications

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“…Its current version, however, does not support bimolecular reactions in 3D space. MCell has recently been extended to run on distributed computing environment to permit large scale simulations [28].…”

Section: Brownian Dynamicsmentioning

confidence: 99%

Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico

2005

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How cells utilize intracellular spatial features to optimize their signaling characteristics is still not clearly understood. The physical distance between the cell-surface receptor and the gene expression machinery, fast reactions, and slow protein diffusion coefficients are some of the properties that contribute to their intricacy. This article reviews computational frameworks that can help biologists to elucidate the implications of space in signaling pathways. We argue that intracellular macromolecular crowding is an important modeling issue, and describe how recent simulation methods can reproduce this phenomenon in either implicit, semi-explicit or fully explicit representation.

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Section: Brownian Dynamicsmentioning

confidence: 99%

Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico

2005

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“…Unlike the next-event simulations approaches described above, particle-based methods employ a discrete fixed time step where particle movement and particle reaction are alternated with successive updates. MCell [36,41] and Smoldyn [37 ] both use a form of Brownian dynamics where the location of any given particle is updated after a small time step by selecting a random direction and a random jump length in accordance with the diffusion constant of the particle. For MCell, particles are only permitted to react at surfaces, a reflection of its original intent as a neural microphysiological tool, in particular, for synaptic transmission location [42].…”

Section: Modelling Stochastic Behaviourmentioning

confidence: 99%

Accommodating space, time and randomness in network simulation

Ridgway

Broderick

Ellison

2006

Current Opinion in Biotechnology

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“…Neuroscientific research has always been devoted to the interplay between morphology and function on various functional levels. Experimental research draws from microscopy techniques that can make morphology and spatio-temporal signals visible (Spacek and Harris, 1997 ; Arellano et al, 2007 ; Chen et al, 2008 ), theoretical work in Computational Neuroscience has brought forth an abundant spread of cellular and network models, many of them rely on a spatial representation of neurons and networks (Bower and Beeman, 1997 ; Hines and Carnevale, 1997 ; Balls et al, 2004 ; Gewaltig and Diesmann, 2007 ; Andrews et al, 2010 ). General purpose simulators such as NEURON or Genesis couple electrical and biochemical models to graph-representations of neurons and synaptically connected networks.…”

Section: Introductionmentioning

confidence: 99%

Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox

et al. 2016

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The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.

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A large scale monte carlo simulator for cellular microphysiology

Abstract: Abstract

Cited by 11 publications

References 24 publications

Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico

Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico

Accommodating space, time and randomness in network simulation

Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox

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