大型的酵母基因網路之統計分析

Abstract

我們是否可能針對大量和複雜的生物網路來發展簡化的模型以獲得深刻的暸解？ 這是後基因體 時代中關於系統生物學的一項重大挑戰。我們計畫針對這項目的發展統計方法，並應用酵母菌方面的 大量基因網路作為研究課題。 首先，我們必須降低統計方法的計算複雜度以分析大量基因網路。 例如， 我們計畫改進降維法 以降低其計算的複雜度，這些方法包括多維尺度法以及相關的非線性的降維法。 這些改進的方法將 用於研究酵母菌的細胞週期及其生物網路分析。 其次，我們必須開發統計方法來研究基因表現曲線，以探討大量的基因網路。 通常基因表現曲 線可能有時間差，所以我們需要進行定位以進一步分群和分類。我們計畫發展統計方法來分析酵母菌 在發酵時雙生長期轉移過程中的基因表現曲線，接著應用布林和貝氏網路進行後續的網路分析。 最後，更具有挑戰性的研究課題是: 發展統計方法來分析大量基因網路中的交互作用模式。由於 交互作用模式的差異，不同實驗技術產生的資料型態將有所不同。因此，我們計畫發展統計方法來整 合不同實驗技術和實驗室的資料，以建立統計的誤差模型，進一步推論與分析酵母菌蛋白質交互作用 網路中的交互作用模式。 透過這個長期研究計畫，我們將發展改進的統計方法，降低其計算的複雜度，針對酵母菌的大量 基因網路，進行降維法研究，網路分析，與交互作用模式偵測。這些方法也可用來研究系統生物學中 人類和其他物種的大量基因網路。

Is it possible to develop simplified models to gain deep insights for large and complex biologic networks? This is a top challenge for system biology in the era of post-genomic studies. We plan to develop statistical methods for this purpose. The large genetic networks in yeast will be used as examples. First of all, it is crucial to reduce the computational complexity of statistical methods for analyzing the large genetic networks. For instance, we plan to develop the improved methods with low computational complexity for dimension reduction techniques, including multi-dimensional scaling (MDS) and related methods in nonlinear dimension reduction. These improved methods will be applied to the analysis of yeast cell cycles and their genetic networks. Secondly, it is often necessary to develop statistical methods to analyze gene expression curves for investigating the large genetic networks. The gene expression curves could have time shifts that will need registration in clustering and classification. We plan to develop statistical methods for analyzing the gene expression curves of diauxic shift in fermentation for yeast. The network analysis by Boolean and Bayesian networks will be used for the follow-up analysis. Finally, it is challenging to develop statistical methods for analyzing the interaction patterns of large genetic networks. The interaction patterns could be distinct and the resulting observation types in various experiment techniques will be different. Hence, we plan to develop statistical methods of estimation and inference for analyzing interaction patterns in yeast protein interaction networks by integrating databases from different experiment techniques and laboratories. At the end of this long term project, we will develop improved statistical methods with low computational complexity for the analysis of dimension reduction, network analysis and interaction pattern in yeast genetic networks. These methods can be applied to study large genetic networks in human and other species for the investigation of system biology.