Department of Physics, University of Houston, Houston, Texas, United States of America.
PLoS One. 2010 Oct 8;5(10):e13080. doi: 10.1371/journal.pone.0013080.
Difficulties associated with implementing gene therapy are caused by the complexity of the underlying regulatory networks. The forms of interactions between the hundreds of genes, proteins, and metabolites in these networks are not known very accurately. An alternative approach is to limit consideration to genes on the network. Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, error estimates of the network make verifiable predictions impossible.
METHODOLOGY/PRINCIPAL FINDINGS: Here, we propose an alternative approach. Rather than attempting to derive an accurate model of the network, we ask what questions can be addressed using lower dimensional, highly simplified models. More importantly, is it possible to use such robust features in applications? We first identify a small group of genes that can be used to affect changes in other nodes of the network. The reduced effective empirical subnetwork (EES) can be computed using steady state measurements on a small number of genetically perturbed systems. We show that the EES can be used to make predictions on expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than of genes.
CONCLUSIONS/SIGNIFICANCE: The constraints imposed by gene expression levels of mutants can be used to address a selected set of questions about a gene network.
与实施基因治疗相关的困难是由潜在调控网络的复杂性引起的。这些网络中数百个基因、蛋白质和代谢物之间的相互作用形式并不是非常准确。另一种方法是将考虑范围限制在网络上的基因上。这些影响网络的稳态测量可以从 DNA 微阵列实验中获得。然而，由于它们包含大量的节点，因此计算影响网络需要进行大量的微阵列实验。此外，网络的误差估计使得可验证的预测变得不可能。
方法/主要发现：在这里，我们提出了一种替代方法。我们不是试图推导出网络的准确模型，而是询问使用低维、高度简化的模型可以解决哪些问题。更重要的是，在应用中是否可以使用这些稳健的特征？我们首先确定一小群基因，这些基因可以用来影响网络中其他节点的变化。使用少数遗传扰动系统的稳态测量可以计算出简化的有效经验子网络（EES）。我们表明，EES 可以用于对其他突变体的表达谱进行预测，并计算如何在基础生物学过程的稳态中实现预定的变化。这些断言在一个合成影响网络中得到了验证。我们还使用以前发表的实验数据计算了与大肠杆菌缺氧网络相关的 EES，并将其用于预测双突变体的基因表达水平。对于不到的基因，预测与实验结果有显著差异。
结论/意义：突变体的基因表达水平所施加的约束可以用来解决关于基因网络的一组选定问题。
