Particle-Based Stochastic Simulation in Systems Biology

@article{Tolle2006ParticleBasedSS,
  title={Particle-Based Stochastic Simulation in Systems Biology},
  author={Dominic P. Tolle and Nicolas Le Nov{\`e}re},
  journal={Current Bioinformatics},
  year={2006},
  volume={1},
  pages={315-320}
}
Computational modeling and simulation have become invaluable tools for the biological sciences. Both aid in the formulation of new hypothesis and supplement traditional experimental research. Many different types of models us- ing various mathematical formalisms can be created to represent any given biological system. Here we review a class of modeling techniques based on particle-based stochastic approaches. In these models, every reacting molecule is repre- sented individually. Reactions… 

Tables from this paper

Biochemical simulations: stochastic, approximate stochastic and hybrid approaches

  • J. Pahle
  • Computer Science
    Briefings Bioinform.
  • 2009
Stochastic simulation methods are systematically reviewed in order to guide the researcher and help her find the appropriate method for a specific problem.

Particle simulations of morphogenesis

This paper discusses recent advances in particle methods for the simulation of biological systems at the mesoscopic and the macroscale level and presents results from applications of particle methods including reaction–diffusion on deforming surfaces, deterministic and stochastic descriptions of tumor growth and angiogenesis.

Computational Methods for the Parallel 3D Simulation of Biochemical Kinetics at the Microscopic Scale

A particle-based system in which each molecular species is represented by a three-dimensional entity which diffuses and may undergo reactions is proposed, suitable for parallel computing and that can especially take advantage of recent multicore and multiprocessor architectures.

Spatial Simulations in Systems Biology: From Molecules to Cells

This review gives an overview of methods which can be used to simulate the complete cell at least with molecular detail, especially Brownian dynamics simulations, and how the atomic level can be included for instance in multi-scale simulation methods.

Visualization and mesoscopic simulation in systems biology

The computational power of recent many-core architectures (CPUs and GPUs) is harnessed for both the simulation and the visualizations of cellular signal transduction, i.e. relaying a signal from outside the cell by different means of transport toward its target inside the cell.

A parallel and distributed discrete event approach for spatial cell-biological simulations

Promises and specific requirements imposed by a spatial simulation of cell biological systems will be illuminated by a parallel and distributed variant of the Next-Subvolume Method (NSM), which augments the Stochastic Simulation Algorithm (SSA) with spatial features, and its realization in a grid-inspired simulation system called Aurora.

Development of a stochastic multi-scale simulation method for the analysis of spatiotemporal dynamics in cellular transport and signaling processes

This work focuses on the development of a stochastic simulation. It employs particle tracking methods to elucidate the transport of signaling molecules through the cell. Furthermore the method is

Parallel Solutions for Voxel-Based Simulations of Reaction-Diffusion Systems

This work discusses aspects for the spatial TAU-leaping in crowded compartments simulator, a voxel-based method for the stochastic simulation of reaction-diffusion processes which relies on the Sτ-DPP algorithm, and presents how the characteristics of the algorithm can be exploited for an effective parallelization on the present heterogeneous HPC architectures.

Impulse-Based Dynamic Simulation of Deformable Biological Structures

The Tethered Particle System is promising for simulations of smallscale self-assembling deformable biological structures exhibiting random motion.

References

SHOWING 1-10 OF 56 REFERENCES

Microbial cell modeling via reacting diffusive particles

A particle-based simulator called ChemCell is described that is developing with the goal of modeling the protein chemistry of biological cells for phenomena where spatial effects are important and interesting computational issues that arise in particle- based cell modeling are highlighted.

Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space.

The main idea of GFRD is to exploit the exact solution of the Smoluchoswki equation to set up an event-driven algorithm, which combines in one step the propagation of the particles in space with the reactions between them.

Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space.

We have developed a new numerical technique, called Green's-function reaction dynamics (GFRD), that makes it possible to simulate biochemical networks at the particle level and in both time and

Stochastic simulation of chemical reactions with spatial resolution and single molecule detail

Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical

Automatic generation of cellular reaction networks with Moleculizer 1.0

This work describes an approach to the exact stochastic simulation of biochemical networks that emphasizes the contribution of protein complexes to these systems and generates much smaller reaction networks, which can be exported to other simulators for further analysis.

Exact Stochastic Simulation of Coupled Chemical Reactions

There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly

STOCHSIM: modelling of stochastic biomolecular processes

SUMMARY STOCHSIM is a stochastic simulator for chemical reactions. Molecules are represented as individual software objects that react according to probabilities derived from concentrations and rate

SmartCell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks.

The results show that this factor might play an important role in the response of networks and cannot be neglected in cell simulations, and the impact of localisation on the behaviour of simple well-defined networks, previously analysed with differential equations.
...