Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series
Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series
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DOI：
10.1371/journal.pcbi.1005234
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发表时间：
2016-12-01
影响因子：
4.3
通讯作者：
Claassen, Manfred
中科院分区：
文献类型：
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作者：
Klimovskaia, Anna;Ganscha, Stefan;Claassen, Manfred
随机化学反应网络构成了一个模型类来定量描述生物系统中的动力学和细胞到细胞的变异性。由于实验的限制，这些网络的拓扑结构通常仅部分表征。目前用于细化网络拓扑的方法是基于对备选拓扑的显式枚举，因此仅限于具有几乎完整知识的小问题实例。我们提出了reactionet lasso，这是一种基于化学主方程推导出逐步稀疏回归方法的计算程序，通过隐式地考虑数十亿个拓扑变体来实现反应网络的大规模结构学习。我们已经评估了结构学习能力的reactionet套索的合成数据的完整的TRAIL诱导的细胞凋亡信号级联反应，包括70个反应。我们发现，reactionet套索是能够有效地恢复这些反应系统的结构，从头算，具有高灵敏度和特异性。只有6000种可能的反应和超过10 2000种网络拓扑。结合信息丰富的单细胞技术，如单细胞RNA测序或质谱细胞术，reactionet lasso将实现大规模的结构学习，特别是在具有部分网络结构知识的领域，如癌症生物学，从而能够检测反应网络的病理变化。我们提供软件以支持reactionet lasso的广泛适用性。
Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only 6000 possible reactions and over 10 2000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.