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Related Experiment Videos

SGN Sim, a stochastic genetic networks simulator.

Andre S Ribeiro1, Jason Lloyd-Price

  • 1Institute for Biocomplexity and Informatics, University of Calgary, Canada. ARibeiro@ucalgary.ca

Bioinformatics (Oxford, England)
|February 3, 2007
PubMed
Summary

SGNSim is a new Stochastic Gene Networks Simulator for modeling gene regulatory networks with time-delayed transcription and translation. This tool enables complex simulations and analysis of genetic system perturbations.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for cellular function.
  • Modeling GRNs requires tools that can handle complex dynamics, including time delays.

Purpose of the Study:

  • To introduce SGNSim, a novel Stochastic Gene Networks Simulator.
  • To provide a flexible platform for modeling gene regulatory networks with time-delayed processes.

Main Methods:

  • Utilizes a stochastic simulation algorithm (SSA) capable of handling multiple time-delayed events.
  • Allows delays to be drawn from various distributions and reaction rates from complex functions or physical parameters.
  • Enables the generation of GRN ensembles and modeling of specific GRNs and chemical reaction systems.

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Main Results:

  • SGNSim can model transcription and translation as multiple time-delayed events.
  • The simulator supports complex reaction rates and delay distributions.
  • Perturbations such as gene deletion, over-expression, copy number changes, and mutations can be modeled.
  • Demonstrated applications include a toggle switch model, a compartmentalized system with feedback, and the yeast transcriptional network.

Conclusions:

  • SGNSim offers a powerful and flexible tool for simulating stochastic gene regulatory networks with time delays.
  • The simulator facilitates the analysis of GRN dynamics under various conditions and perturbations.
  • SGNSim can be applied to diverse biological systems, from simple genetic switches to complex transcriptional networks.