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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

Portuguese

A descoberta de redes de dados de reação estocástica única a partir de dados é central para a biologia de célula única. A inferência baseada em simulação evoluiu para um paradigma flexível para este fim, contornando a intractabilidade computacional da probabilidade. Notavelmente, a computação bayesiana aproximada realiza esta inferência, realizando extensas simulações de Gillespie, geralmente a um alto custo computacional. Aqui, propomos um quadro de computação bayesiana aproximada sem simulação (SFABC) que evita tanto a simulação quanto as soluções de probabilidade explícita da dinâmica da rede. Em vez disso, a SFABC explora a equação principal química para formular uma otimização convexa tratável com as restrições resultantes, que são então us

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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