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Parameter estimation and model selection in computational biology.

Gabriele Lillacci1, Mustafa Khammash

  • 1Center for Control, Dynamical Systems and Computation, University of California at Santa Barbara, Santa Barbara, California, United States of America.

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Summary
This summary is machine-generated.

This study introduces a novel method using an extended Kalman filter for accurate biological model parameter estimation from noisy data. The approach refines parameter guesses and can also help select the best model for a biological system.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Determining parameters for computational models of biological systems is a central challenge.
  • Experimental measurements are often noisy and limited, making parameter fitting difficult.

Purpose of the Study:

  • To present a new approach for parameter selection in biological models using dynamic recursive estimators.
  • To demonstrate the utility of the extended Kalman filter for estimating model parameters from noisy biological data.

Main Methods:

  • Utilized a variation of the extended Kalman filter for initial parameter estimation.
  • Employed an a posteriori identifiability test to assess the reliability of parameter estimates.
  • Applied an optimization problem to refine initial parameter estimates for improved accuracy.

Main Results:

  • Achieved statistically consistent parameter estimates with experimental measurements.
  • Demonstrated the method's ability to discriminate among alternative biological models.
  • Successfully applied the approach to models of E. coli heat shock response and synthetic gene regulation.

Conclusions:

  • The extended Kalman filter provides a robust method for parameter estimation in biological models with noisy data.
  • The developed tools are general and applicable to a wide range of biological systems for parameter estimation and model selection.