Integrative modeling of genetic interactions based on intertwined multi-level networks

基因一直被认为是可以单独分析的因素，它们对功能和表型的影响可以线性建模。然而，现在它们被认为是与其他基因和其他类型的生物分子非线性相互作用的实体，它们的作用和控制将比我们想象的更加复杂和依赖于环境。本研究将基因视为各种生物分子相互交织的多层次网络的构建块，并开发了通过网络综合分析行为的计算方法，以及将单个构建块的定量测量与相应网络的已知结构相关联的计算方法。最近的研究产生了大量的数据，如转录组、代谢组、蛋白质组和通量组，许多生物网络数据库正在开发中。因此，需要建立综合计算方法，以利用各种网络及其组成部分上的这些丰富数据。

Genes had been considered to be factors that can be analyzed individually, and their effect on functions and phenotypes can be modeled linearly. However, now they are rather considered as non-linearly interacting entities to other genes and other types of biomolecules whose roles and control would be more complicated and context-dependent than we thought. This research regards genes as building blocks of intertwined multi-level networks of various biomolecules, and develops computational methods for integratively analyzing behaviors thorough the networks and for relating quantitative measurements of individual building blocks to the known structures of corresponding networks. Recent studies are producing large data such as transcriptomes, metabolomes, proteomes, and fluxomes, and many databases on biological networks are being developed. The integrative computational approaches thus need to be established to leverage these rich data on various networks and their constituents.