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Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated

Qianqian Wu, Kate Smith-Miles, Tianhai Tian

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    This study introduces a new algorithm for parameter estimation in stochastic biological models, improving accuracy using simulated likelihood density. The method enhances parameter inference from sparse data in systems biology.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Mathematical Modeling

    Background:

    • Mathematical modeling is crucial for understanding dynamic properties in complex biological systems.
    • Inferring parameters in mathematical models from sparse and stochastic experimental data presents a significant challenge in systems biology.

    Purpose of the Study:

    • To develop and validate a novel algorithm for parameter estimation in stochastic mathematical models.
    • To enhance the accuracy of parameter inference, particularly for sparse and noisy biological data.

    Main Methods:

    • Proposed a new algorithm utilizing simulated likelihood density within the framework of approximate Bayesian computation.
    • Employed two distinct stochastic models to rigorously test the algorithm's efficiency and effectiveness.
    • Developed a secondary algorithm with a novel objective function to assess the accuracy of stochastic simulations.

    Main Results:

    • The proposed method significantly improves the accuracy of parameter estimates.
    • Utilizing simulated likelihood density leads to more precise parameter inference compared to traditional methods.
    • Assessing error at individual observation time points yields superior parameter accuracy.

    Conclusions:

    • Simulated likelihood density is a key factor in enhancing the accuracy of parameter estimation for stochastic models.
    • The developed algorithm outperforms existing methods, especially when dealing with sparse data and stochasticity.
    • Individual error measurement at observation points improves parameter estimation accuracy.