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Literature DB >> 15852513

Dizzy: stochastic simulation of large-scale genetic regulatory networks.

Stephen Ramsey¹, David Orrell, Hamid Bolouri.

Abstract

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.

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Year: 2005 PMID： 15852513 DOI： 10.1142/s0219720005001132

Source DB: PubMed Journal: J Bioinform Comput Biol ISSN： 0219-7200 Impact factor: 1.122

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Cited

65 in total

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2. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim.

Authors: Michael W Sneddon; James R Faeder; Thierry Emonet
Journal: Nat Methods Date: 2010-12-26 Impact factor: 28.547

3. Simulations of stochastic biological phenomena.

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4. Adaptive-control model for neutrophil orientation in the direction of chemical gradients.

Authors: Daniel Irimia; Gábor Balázsi; Nitin Agrawal; Mehmet Toner
Journal: Biophys J Date: 2009-05-20 Impact factor: 4.033

Review 5. Consistent design schematics for biological systems: standardization of representation in biological engineering.

Authors: Yukiko Matsuoka; Samik Ghosh; Hiroaki Kitano
Journal: J R Soc Interface Date: 2009-06-03 Impact factor: 4.118

Dizzy: stochastic simulation of large-scale genetic regulatory networks.

1. Broadly heterogeneous activation of the master regulator for sporulation in Bacillus subtilis.

2. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim.

3. Simulations of stochastic biological phenomena.

4. Adaptive-control model for neutrophil orientation in the direction of chemical gradients.

Review 5. Consistent design schematics for biological systems: standardization of representation in biological engineering.

6. Benchmarking regulatory network reconstruction with GRENDEL.

7. Programming with models: modularity and abstraction provide powerful capabilities for systems biology.

8. Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression.

9. Biochemical simulations: stochastic, approximate stochastic and hybrid approaches.

10. Computational limits to binary genes.