Chen Chung-Ming, Lee Chih, Chuang Cheng-Long, Wang Chia-Chang, Shieh Grace S
Institute of Statistical Science, Academia Sinica, No 128, Sec 2, Academia Road, Taipei 115, Taiwan.
BMC Syst Biol. 2010 Feb 26;4:16. doi: 10.1186/1752-0509-4-16.
Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.
An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.
GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.
生化途径正逐渐被视为复杂人类疾病的核心，最近基因/转录相互作用已被证明能够预测部分途径。随着微阵列基因表达数据（MGED）提供的丰富信息，对这些相互作用进行非线性建模现在变得可行。非线性建模的两个最新进展使用S形模型来描述转录因子（TF）对靶基因的转录相互作用，但未对多个TF对一个靶标的协同或竞争相互作用进行建模。
开发了一种S形模型和一种优化算法（GASA），以使用MGED同时推断多个基因的遗传相互作用/转录调控。GASA由遗传算法（GA）和模拟退火（SA）算法组成，并通过最陡梯度下降算法进行增强，以避免陷入局部最小值。使用具有不同噪声程度的模拟数据，我们研究了具有两种模型选择标准和两个搜索空间的GASA的性能。此外，GASA被证明优于网络组件分析、时间序列网络推理算法（TSNI）、具有常规GA的GA（GAGA）和具有常规SA的GA。展示了两个应用。首先，GASA被应用于推断人类T细胞凋亡的一个子网。一些预测的相互作用得到了文献的支持。其次，GASA被应用于推断酿酒酵母中34个细胞周期调控靶标的转录因子，并且GASA的表现优于非线性建模的最新进展之一、GAGA和TSNI。此外，GASA能够预测某些靶标的多个转录因子，并且这些结果与YEASTRACT中的实验确认数据一致。
GASA被证明能够很好地推断遗传相互作用和转录调控相互作用。特别是，GASA似乎能够表征酵母中转录调控相互作用（TI）的非线性机制，并且可能应用于推断其他生物体中的TI。人类T细胞凋亡子网的预测遗传相互作用与现有的部分途径一致，表明GASA在推断生化途径方面的潜力。
