随机化学反应网络的自动深度抽象
Automated Deep Abstractions for Stochastic Chemical Reaction Networks
摘要：
从分子相互作用的机械模型中预测随机细胞动力学是系统生物学中的一个长期挑战：低级化学反应网络（CRN）模型引发了高维连续时间马尔可夫链（CTMC），其计算要求很高，并且通常在实践中无法分析。最近提出的一种抽象方法使用深度学习来用离散时间连续空间过程取代CTMC，方法是用以规则时间间隔采样的迹线（可以通过模拟给定的CRN或从实验中获得的时间序列数据来获得）训练混合密度深度神经网络。这种抽象的主要优点是，它产生了一个计算模型，执行起来非常便宜，同时保留了训练数据的统计特征。一般来说，抽象准确性随着训练数据量的增加而提高。然而，根据CRN，方法的整体质量-效率增益和抽象精度-也将取决于由超参数（例如层类型和它们之间的连接）给出的神经网络架构的选择。因此，在实践中，建模人员必须通过繁琐而耗时的反复试验来手动为每个给定的CRN找到合适的架构。在本文中，我们建议通过学习最佳神经网络架构以及学习抽象过程的转换内核，进一步自动化随机CRN的深度抽象。对架构的自动搜索使该方法直接适用于任何给定的CRN，这对于深度学习专家来说是节省时间的，对于非专家来说是至关重要的。我们实现了该方法，并证明了其性能上的一些代表性的CRN与多模态紧急表型。
英文摘要：
Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical reaction network (CRN) models give raise to a highly-dimensional continuous-time Markov chain (CTMC) which is computationally demanding and often prohibitive to analyse in practice. A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process, by training a mixture density deep neural network with traces sampled at regular time intervals (which can obtained either by simulating a given CRN or as time-series data from experiment). The major advantage of such abstraction is that it produces a computational model that is dramatically cheaper to execute, while preserving the statistical features of the training data. In general, the abstraction accuracy improves with the amount of training data. However, depending on a CRN, the overall quality of the method -- the efficiency gain and abstraction accuracy -- will also depend on the choice of neural network architecture given by hyper-parameters such as the layer types and connections between them. As a consequence, in practice, the modeller would have to take care of finding the suitable architecture manually, for each given CRN, through a tedious and time-consuming trial-and-error cycle. In this paper, we propose to further automatise deep abstractions for stochastic CRNs, through learning the optimal neural network architecture along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. We implement the method and demonstrate its performance on a number of representative CRNs with multi-modal emergent phenotypes.
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