Bayesian inference of distributed time delay in transcriptional and translational regulation
Bayesian inference of distributed time delay in transcriptional and translational regulation
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DOI：
10.1093/bioinformatics/btz574
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发表时间：
2020-01-15
期刊：
影响因子：
5.8
通讯作者：
Kim, Jae Kyoung
中科院分区：
文献类型：
--
作者：
Choi, Boseung;Cheng, Yu-Yu;Kim, Jae Kyoung
动机：实验和成像技术的进步使人们能够前所未有地深入了解单个细胞内的动态过程。然而，细胞内动力学的许多方面仍然隐藏着，或者只能间接测量。这使得重建管理各种细胞功能下的生化过程的调控网络变得具有挑战性。目前推断反应速率的估计技术通常依赖于对未观察到的过程和状态的边际化。即使在简单的系统中，这种方法也可能在计算上具有挑战性，并且可能导致参数估计中的巨大不确定性和缺乏稳健性。因此，我们需要其他的方法来有效地发现复杂生化网络中的相互作用。结果：我们提出了一种贝叶斯推理框架，该框架基于用时间延迟替换不感兴趣的或未观察到的反应。虽然所得到的模型是非马尔可夫的，但最近关于随机时滞系统的结果使我们能够严格地获得模型参数的似然表达式。反过来，这使我们能够扩展MCMC方法，从单细胞分析中有效地估计反应速度和延迟分布参数。我们用合成和实验数据说明了使用生灭模型的方法的优点和潜在的缺陷，并表明我们可以使用相对较少的测量来稳健地推断模型参数。我们演示了如何做到这一点，即使只测量了细胞内的相对分子计数，就像荧光显微镜的情况一样。
Motivation: Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.Results: We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy.