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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

Berkeley-Madonna implementation of Ikeda's model.

J Fontecave-Jallon1, P Baconnier

  • 1PRETA Team (Physiologie Respiratoire Expérimentale Théorique et Appliquée), TIMC, Faculté de Médecine, La Tronche cedex, France. Julie.Fontecave@imag.fr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary

Researchers successfully implemented Ikeda

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Area of Science:

  • Physiology
  • Computational Biology
  • Biomathematics

Background:

  • Mathematical models are crucial for understanding complex biological systems.
  • The Ikeda et al. model (1979) provides a mathematical framework for fluid regulation.
  • Evaluating new software for biological model simulation is essential.

Purpose of the Study:

  • To assess the feasibility of using Berkeley Madonna software for biological model simulation.
  • To transpose and simulate the Ikeda et al. fluid regulation model using Berkeley Madonna.
  • To verify the software's capability in reproducing existing simulation results.

Main Methods:

  • The Ikeda et al. fluid regulation model was selected for transposition.
  • Berkeley Madonna software was utilized for implementation and simulation.
  • Simulation results were compared against the original paper's findings.

Main Results:

  • The Ikeda et al. model was successfully implemented in Berkeley Madonna.
  • Original simulation results were accurately reproduced.
  • The software facilitated easy execution of new simulations.

Conclusions:

  • Berkeley Madonna is a viable and user-friendly tool for simulating biological integrated models.
  • The software simplifies the process of verifying and extending existing biological models.
  • This demonstrates the utility of Berkeley Madonna for physiological modeling research.