We propose a novel Markov chain Monte‐Carlo (MCMC) method for reverse engineering the topological structure of stochastic reaction networks, a notoriously challenging problem that is relevant in many modern areas of research, like discovering gene regulatory networks or analyzing epidemic spread. The method relies on projecting the original time series trajectories, from the stochastic data generating process, onto information rich summary statistics and constructing the appropriate synthetic likelihood function to estimate reaction rates. The resulting estimates are consistent in the large volume limit and are obtained without employing complicated tuning strategies and expensive resampling as typically used by likelihood‐free MCMC and approximate Bayesian methods. To illustrate the method, we apply it in two real data examples: the molecular pathway analysis with RNA‐seq and the famous incidence data from 1665 plague outbreak at Eyam, England.

中文翻译：

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反应网络推理的综合似然法

我们提出了一种新颖的马尔可夫链蒙特卡罗（MCMC）方法，用于对随机反应网络的拓扑结构进行逆向工程，这是一个臭名昭著的挑战性问题，与发现现代基因调控网络或分析流行病传播等许多现代研究领域相关。该方法依赖于将随机数据生成过程中的原始时间序列轨迹投影到信息量丰富的汇总统计数据上，并构造适当的合成似然函数来估计反应速率。结果估计在大音量范围内是一致的，并且无需采用复杂的调整策略和昂贵的重采样（如无可能性MCMC和近似贝叶斯方法所通常使用的）即可获得。为了说明该方法，我们将其应用于两个实际数据示例：