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Exploiting intrinsic fluctuations to identify model parameters.

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    This summary is machine-generated.

    This study introduces a novel method to resolve non-identifiable model parameters in computational systems biology. By incorporating stochastic fluctuations, previously unidentifiable parameters become identifiable, even with measurement noise.

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    Area of Science:

    • Computational Systems Biology
    • Biochemical Kinetics
    • Mathematical Modeling

    Background:

    • Parameter estimation in kinetic models is crucial for computational systems biology.
    • Structural non-identifiability of model parameters is a significant challenge, often due to functional relationships.
    • Measurement noise is typically viewed as a hindrance to parameter estimation.

    Purpose of the Study:

    • To present a method for identifying structurally non-identifiable model parameters within a deterministic framework.
    • To leverage intrinsic fluctuations in particle numbers to resolve parameter non-identifiability.
    • To demonstrate the method's effectiveness on various biochemical system models.

    Main Methods:

    • Utilizing time-course recordings of biochemical systems (steady or transient states) as input.
    • Developing an objective function that includes a measure for fluctuations in particle numbers.
    • Applying the method to partially observed recordings with measurement noise.

    Main Results:

    • The proposed method successfully resolves parameter non-identifiability issues.
    • Effectiveness demonstrated on immigration-death, gene expression, and Epo-EpoReceptor interaction models.
    • The approach remains effective even with measurement noise of known amplitude.

    Conclusions:

    • Intrinsic fluctuations can render previously non-identifiable parameters identifiable.
    • The method offers a simple, fast, and effective solution for parameter identification in biochemical systems.
    • Optimization can be achieved through classical or Bayesian approaches.