Sontag Eduardo, Kiyatkin Anatoly, Kholodenko Boris N
Department of Mathematics, Rutgers University, Piscataway, NJ 08854, USA.
Bioinformatics. 2004 Aug 12;20(12):1877-86. doi: 10.1093/bioinformatics/bth173. Epub 2004 Mar 22.
High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions.
We demonstrate that all connections leading to a given network node, e.g. to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g. using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks.
Supplementary material is available at http://www.dbi.tju.edu/bioinformatics2004.pdf
高通量技术促进了大型基因组学和蛋白质组学数据集的获取。然而，这些数据提供的是细胞行为的快照，而非帮助我们揭示因果关系。在此，我们提出如何通过监测细胞网络对实验干预的时间依赖性反应，利用这些技术推断基因、蛋白质和代谢物之间连接的拓扑结构和强度。
我们证明，所有通向给定网络节点（例如特定基因）的连接，都可以从对扰动的反应中推导出来，其中没有一个扰动直接影响该节点（例如使用对其他基因进行基因敲除的菌株）。为了从静态数据推断所有相互作用，每个节点应单独或与其他节点组合进行扰动。监测时间序列可提供更丰富的信息，且不需要对所有节点进行扰动。总体而言，我们提出的方法能够推导和量化细胞内基因、信号和代谢网络内部及之间的功能相互作用。
