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Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks

机译:基于矩的随机生化反应网络参数推断方法与实验设计

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Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.
机译:在实践中,连续时间马尔可夫链通常用于建模生化反应网络,其中分子相互作用的固有随机性不可忽略。这促使最近的研究工作进入了用于此类模型的参数推断和实验设计的方法。主要的困难在于,这样的方法通常需要一个方法来迭代求解控制系统概率分布的时间演化的化学主方程。然而,这几乎是不可能的,甚至近似技术仍然限于相对较小和简单的系统。本文探讨的另一种方法是将方法仅基于整个概率分布的一些低阶矩。我们总结了这种基于矩的参数推断和实验设计方法背后的理论,并提供了新的案例研究来研究其性能。

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