Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul 120-749, Republic of Korea.
Prog Biophys Mol Biol. 2011 Aug;106(2):435-42. doi: 10.1016/j.pbiomolbio.2011.01.003. Epub 2011 Jan 31.
A cellular system may be viewed as a social network of genes. Genes work together to conduct physiological processes in the cells. Thus if we have a view of the functional association among genes, we may also be able to unravel the association between genotypes and phenotypes; the emergent properties of interactive activities of genes. We could have various points of view for a gene network. Perhaps the most common standpoints are protein-protein interaction networks (PPIN) and transcriptional regulatory networks (TRN). Here I introduce another type of view for the gene network; the probabilistic functional gene network (PFGN). A 'functional view' of association between genes enables us to have a holistic model of the gene society. A 'probabilistic view' makes the model of gene associations derived from noisy high-throughput data more robust. In addition, the dynamics of gene association may be presented in a single static network model by the probabilistic view. By combining the two modeling views, the probabilistic functional gene networks have been constructed for various organisms and proved to be highly useful in generating novel biological hypotheses not only for simple unicellular microbes, but also for highly complex multicellular animals and plants.
一个细胞系统可以被看作是基因的社交网络。基因协同工作以在细胞中进行生理过程。因此，如果我们对基因之间的功能关联有一个了解，我们也可能能够揭示基因型和表型之间的关联；这是基因相互作用活动的涌现特性。我们可以从各种角度来看待基因网络。也许最常见的观点是蛋白质-蛋白质相互作用网络（PPIN）和转录调控网络（TRN）。在这里，我介绍基因网络的另一种视角；概率功能基因网络（PFGN）。基因之间关联的“功能视角”使我们能够拥有基因社会的整体模型。“概率视角”使源自嘈杂的高通量数据的基因关联模型更加稳健。此外，通过概率视图，可以在单个静态网络模型中呈现基因关联的动态。通过结合这两种建模观点，已经为各种生物体构建了概率功能基因网络，并证明它们在生成新的生物学假设方面非常有用，不仅适用于简单的单细胞微生物，也适用于高度复杂的多细胞动物和植物。
