Algebraic Statistical Model for Biochemical Network Dynamics Inference.
Algebraic Statistical Model for Biochemical Network Dynamics Inference.
复制标题
生化网络动力学推理的代数统计模型。
DOI：
10.1166/jcsmd.2013.1032
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Rempala,GrzegorzA
中科院分区：
文献类型：
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作者：
Linder,DanielF;Rempala,GrzegorzA
使用现代分子定量方法，例如，高通量测序，生物学家可以进行多个复杂的实验并收集RNA和DNA浓度的纵向数据。这样的数据可以用来推断感兴趣的分子实体之间的细胞水平的相互作用。一种形式化这种推断的方法是[2]的化学计量代数统计模型（SASM），它允许分析所谓的二次（或单源）网络。尽管它具有直观的吸引力，但到目前为止，SASM仅在少数简单的例子上进行了启发式研究。本文提供了一种更正式的SASM数学处理方法，将原始模型扩展到更广泛的可分解为多个圆锥子网络的反应系统。特别地，这里证明了在这样的网络上，SASM享有所谓的稀疏性，也就是说，它通过将它们的反应速率设置为零，渐近地（随着观察到的网络轨迹的数量）抛弃虚假的相互作用。举例来说，我们将扩展的SASM应用于来自一般可分解网络的计算机数据，以及来自斑马鱼视网膜中热休克蛋白70 （Hsp70）可能转录因子的实验搜索的生物学数据。
With modern molecular quantification methods, like, for instance, high throughput sequencing, biologists may perform multiple complex experiments and collect longitudinal data on RNA and DNA concentrations. Such data may be then used to infer cellular level interactions between the molecular entities of interest. One method which formalizes such inference is the stoichiometric algebraic statistical model (SASM) of [2] which allows to analyze the so-called conic (or single source) networks. Despite its intuitive appeal, up until now the SASM has been only heuristically studied on few simple examples. The current paper provides a more formal mathematical treatment of the SASM, expanding the original model to a wider class of reaction systems decomposable into multiple conic subnetworks. In particular, it is proved here that on such networks the SASM enjoys the so-called sparsistency property, that is, it asymptotically (with the number of observed network trajectories) discards the false interactions by setting their reaction rates to zero. For illustration, we apply the extended SASM to in silico data from a generic decomposable network as well as to biological data from an experimental search for a possible transcription factor for the heat shock protein 70 (Hsp70) in the zebrafish retina.