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Performance of objective functions and optimisation procedures for parameter estimation in system biology models.

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Data-driven normalisation of simulations improves parameter estimation in large biological models. This method enhances optimisation algorithm convergence and reduces non-identifiability, making it preferable for complex systems.

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Area of Science:

  • Systems biology
  • Synthetic biology
  • Mathematical modeling

Background:

  • Large dynamic models with numerous parameters are crucial for systems and synthetic biology.
  • Parameter estimation in dynamic systems often uses scaling factors, which can increase non-identifiability.

Purpose of the Study:

  • To investigate the impact of unknown parameter count on optimisation algorithm convergence.
  • To compare data-driven normalisation with scaling factors for objective functions in mathematical modelling.

Main Methods:

  • Evaluated three optimisation algorithms and four objective functions.
  • Compared simulations normalised to data versus simulations multiplied by scaling factors.
  • Assessed convergence speed and parameter non-identifiability.

Main Results:

  • Scaling factors increase practical non-identifiability compared to data-driven normalisation.
  • Data-driven normalisation significantly improves convergence speed for large parameter sets (74 parameters).
  • Data-driven normalisation enhances performance of non-gradient-based algorithms even with fewer parameters (10 parameters).

Conclusions:

  • Data-driven normalisation of simulations is a preferred approach for large biological models.
  • This method mitigates non-identifiability issues and ensures timely parameter estimation.
  • It offers advantages for both small and large numbers of unknown parameters.