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Area of Science:

  • Computational Biology
  • Systems Biology
  • Machine Learning

Background:

  • Stochastic Petri Nets (SPNs) are vital for modeling discrete-event systems in fields like epidemiology and systems biology.
  • Parameter estimation in SPNs is difficult, especially with covariate-dependent rates and missing likelihoods.

Purpose of the Study:

  • To introduce a neural-surrogate framework for accurate parameter estimation in partially observed SPNs.
  • To address challenges in modeling systems where explicit likelihoods are unavailable and transition rates depend on covariates.

Main Methods:

  • A neural-surrogate framework using a 1D Convolutional Residual Network was developed.
  • The model was trained end-to-end on Gillespie-simulated SPN realizations, learning to invert system dynamics from noisy trajectories.
  • Monte Carlo dropout was utilized for uncertainty quantification during inference.

Main Results:

  • The surrogate model accurately recovered rate-function coefficients (RMSE = 0.108) on synthetic SPNs with 20% missing events.
  • The neural-surrogate approach demonstrated significantly faster performance compared to traditional Bayesian methods.
  • Calibrated uncertainty bounds were provided alongside point estimates.

Conclusions:

  • Data-driven, likelihood-free neural surrogates enable robust and real-time parameter recovery in complex, partially observed discrete-event systems.
  • This framework enhances the applicability of SPNs in fields requiring efficient and accurate parameter estimation from incomplete data.