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Simulation-free approximate Bayesian computation for stochastic reaction networks

Zekai Li, Mauricio Barahona, Philipp Thomas · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26361 · cited by 0 works (OpenAlex) · open-access PDF

French

Découvrir les réseaux de données de réaction stochastiques uniques à partir de données est central à la biologie des cellules uniques. L'inference basée sur la simulation a évolué dans un paradigme flexible à cet effet, contournant l'intractabilité computationnelle de la probabilité. Notamment, le calcul Bayesian approximatif exécute cette inference en effectuant des simulations Gillespie étendues, généralement à un coût calculé élevé. Ici, nous proposons un cadre de calcul Bayesian approximatif (SFABC) sans simulation qui évite à la fois la simulation et les solutions explicites de probabilité de la dynamique du réseau. Au lieu de cela, SFABC exploite l'équation majeure chimique pour formuler une optimisation convexe trac

Machine translation (Meta M2M100); the English original follows.

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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Topics: Approximate Bayesian computation, Bayesian network, Computer science, Computation, Inference, Bayesian inference

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