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Parameter estimation methods for chaotic intercellular networks.

Inés P Mariño1, Ekkehard Ullner, Alexey Zaikin

  • 1Departamento de Física, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain.

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|November 28, 2013
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Summary
This summary is machine-generated.

We explored simulation techniques for parameter estimation in chaotic intercellular networks. These methods, including approximate Bayesian computation, accurately estimate parameters in coupled chaotic systems.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Chaotic intercellular networks play crucial roles in biological processes.
  • Accurate parameter estimation is essential for understanding and modeling these complex systems.
  • Traditional methods often struggle with the inherent noise and dynamics of chaotic biological networks.

Purpose of the Study:

  • To investigate simulation-based techniques for parameter estimation in chaotic intercellular networks.
  • To combine synchronization-based frameworks with advanced computational inference methods.
  • To evaluate the effectiveness and accuracy of proposed methods.

Main Methods:

  • Developed a synchronization-based framework for parameter estimation in coupled chaotic systems.
  • Employed stochastic optimization (accelerated random search) and approximate Bayesian computation (ABC).
  • Utilized Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) schemes within the ABC framework.

Main Results:

  • Accurate parameter estimates were obtained by averaging over MCMC realizations.
  • Accurate parameter estimates were achieved by averaging over the final population in the SMC scheme.
  • Computational effectiveness of the methods was analyzed using a network of two coupled modified repressilators.

Conclusions:

  • Simulation-based techniques, particularly those leveraging approximate Bayesian computation, are effective for parameter estimation in chaotic intercellular networks.
  • The proposed synchronization-based framework combined with MCMC and SMC offers robust and accurate parameter inference.
  • These methods provide valuable tools for understanding complex biological systems with chaotic dynamics.