Simulation-free approximate Bayesian computation for stochastic reaction networks
Abstract
Uncovering stochastic reaction networks from data is central to single-cell biology. Simulation-based inference has evolved into a flexible paradigm for this purpose, circumventing the computational intractability of the likelihood. Notably, approximate Bayesian computation performs this inference by carrying out extensive Gillespie simulations, usually at a high computational cost. Here, we propose a simulation-free approximate Bayesian computation (SFABC) framework that avoids both simulation and explicit likelihood solutions of the network dynamics. Instead, SFABC exploits the Chemical Master Equation to formulate a tractable convex optimization with ensuing constraints, which are then used to accept sampled parameters consistent with the observed data. The resulting posterior approximation comes with theoretical guarantees: it can be tightened systematically, and it provably converges to the exact Bayesian posterior distributions. Using synthetic benchmarks, we show that SFABC matches the accuracy of standard simulation-based algorithms at a substantially reduced computational cost. We then showcase the applicability of SFABC to experimental systems biology by analyzing gene transcription bursting kinetics from snapshot, time-resolved, and cell-cycle-resolved single-cell data.
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BibTeX
@misc{li2026simulationfree,
title = {{Simulation-free approximate Bayesian computation for stochastic reaction networks}},
author = {Zekai Li and Mauricio Barahona and Philipp Thomas},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26361},
eprint = {2610.26361},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26361},
}
RIS
TY - UNPB
TI - Simulation-free approximate Bayesian computation for stochastic reaction networks
AU - Zekai Li
AU - Mauricio Barahona
AU - Philipp Thomas
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26361
UR - https://arxiv.org/abs/2610.26361
AB - Uncovering stochastic reaction networks from data is central to single-cell biology. Simulation-based inference has evolved into a flexible paradigm for this purpose, circumventing the computational intractability of the likelihood. Notably, approximate Bayesian computation performs this inference by carrying out extensive Gillespie simulations, usually at a high computational cost. Here, we propose a simulation-free approximate Bayesian computation (SFABC) framework that avoids both simulation and explicit likelihood solutions of the network dynamics. Instead, SFABC exploits the Chemical Master Equation to formulate a tractable convex optimization with ensuing constraints, which are then used to accept sampled parameters consistent with the observed data. The resulting posterior approximation comes with theoretical guarantees: it can be tightened systematically, and it provably converges to the exact Bayesian posterior distributions. Using synthetic benchmarks, we show that SFABC matches the accuracy of standard simulation-based algorithms at a substantially reduced computational cost. We then showcase the applicability of SFABC to experimental systems biology by analyzing gene transcription bursting kinetics from snapshot, time-resolved, and cell-cycle-resolved single-cell data.
ER -