Newest papers

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

Chinese

从数据中揭露单个斯托加斯特反应数据网络是单细胞生物学的核心。模拟基于推断已经发展成为一个灵活的范式,绕过了概率的计算不可追溯性。值得注意的是,大致的贝叶斯基计算通过进行广泛的Gillespie模拟,通常以高计算成本进行这种推断。在这里,我们提出了一个模拟自由的Bayesian Approximate Calculation(SFABC)框架,避免了网络动态的模拟和明确的概率解决方案。相反,SFABC利用化学主方程式来制定可追溯的圆形优化,随之而来的限制,然后用来接受与观察到的数据相符的样本参数。结果的后续接近带来了理论保证:它可以系统地收紧,并且可以

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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