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    Area of Science:

    • Computational Biology
    • Biophysics
    • Systems Biology

    Background:

    • Parameter inference for Markov jump processes (MJPs) in stochastic kinetic models is challenging due to intractable transition probabilities.
    • Bayesian inference often relies on computationally intensive methods like particle Markov Chain Monte Carlo (MCMC), requiring simulation of conditioned jump processes.
    • Highly informative observations can render standard forward simulation inefficient or infeasible for exact analysis.

    Purpose of the Study:

    • To develop efficient methods for simulating conditioned jump processes for improved parameter inference.
    • To address the computational bottlenecks in Bayesian inference for MJPs when dealing with informative data.
    • To enable more robust analysis of stochastic kinetic models in biological systems.

    Main Methods:

    • Proposed three novel methods to enhance the efficiency of simulating conditioned jump processes.
    • Derived a conditioned hazard based on an approximation to the jump process for generating end-point conditioned trajectories within an importance sampling algorithm.
    • Adapted a sequential Monte Carlo (SMC) scheme, reweighting trajectories at intermediate time points based on consistency with observations, using two continuous MJP approximations.

    Main Results:

    • Compared the performance of the proposed methods using a tractable jump process.
    • Successfully applied the best-performing method for parameter inference in a Lotka-Volterra system.
    • Inferred parameters for a Bacillus subtilis motility regulation model using the optimized approach.

    Conclusions:

    • The developed methods significantly improve the efficiency of simulating conditioned jump processes.
    • These advancements facilitate more effective Bayesian parameter inference for MJPs, even with highly informative data.
    • The approach is validated for biological systems, including Lotka-Volterra and bacterial motility regulation models.