IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov-Dec;15(6):1754-1764. doi: 10.1109/TCBB.2016.2635646. Epub 2016 Dec 5.
Gene regulatory networks are a global representation of complex interactions between molecules that dictate cellular behavior. Study of a regulatory network modulated by single or multiple modulators' expression levels, including microRNAs (miRNAs) and transcription factors (TFs), in different conditions can further reveal the modulators' roles in diseases such as cancers. Existing computational methods for identifying such modulated regulatory networks are typically carried out by comparing groups of samples dichotomized with respect to the modulator status, ignoring the fact that most biological features are intrinsically continuous variables. Here, we devised a sliding window-based regression scheme and proposed the Regression-based Inference of Modulation (RIM) algorithm to infer the dynamic gene regulation modulated by continuous-state modulators. We demonstrated the improvement in performance as well as computation efficiency achieved by RIM. Applying RIM to genome-wide expression profiles of 520 glioblastoma multiforme (GBM) tumors, we investigated miRNA- and TF-modulated gene regulatory networks and showed their association with dynamic cellular processes and brain-related functions in GBM. Overall, the proposed algorithm provides an efficient and robust scheme for comprehensively studying modulated gene regulatory networks.
基因调控网络是分子间复杂相互作用的全局表示，决定着细胞的行为。研究受单个或多个调节剂（包括 microRNAs (miRNAs) 和转录因子 (TFs)）表达水平调节的调控网络，在不同条件下，可以进一步揭示调节剂在癌症等疾病中的作用。现有的用于识别这种调节的调控网络的计算方法通常是通过比较关于调节剂状态的二分类样本组来进行的，忽略了大多数生物学特征本质上是连续变量的事实。在这里，我们设计了一种基于滑动窗口的回归方案，并提出了基于回归的调制推断（RIM）算法，以推断由连续状态调节剂调节的动态基因调控。我们证明了 RIM 所取得的性能和计算效率的提高。将 RIM 应用于 520 例胶质母细胞瘤（GBM）肿瘤的全基因组表达谱，我们研究了 miRNA 和 TF 调节的基因调控网络，并显示了它们与 GBM 中动态细胞过程和与大脑相关的功能的关联。总的来说，所提出的算法为全面研究调节基因调控网络提供了一种高效、稳健的方案。
