運用整合性的統計分析探討代謝網路演化的機制

Abstract

近年來，根據分子演化學的研究，生物系統的複雜度主要來自於系統內共通的子單元的重組。本論文提出一個對於代謝網路演化的研究架構。做為一個延伸的研究，我們為代謝網路建構一個新模型，並運用整合性的統計分析來探究代謝網路的演化。基於統計分析的結果，我們對於代謝網路演化的機制推導出幾個主要的規則，並且對於研究特定的代謝途徑提供一些看法。 這個研究是關於中央研究院統計科學研究所楊振翔博士先前所做之研究進行延伸的探討，並且描述了一個對於代謝網路演化具有延伸應用可能的整合性統計分析。

In recent years, studies on molecular evolution suggested that the complexity of biological systems arises from recombination of common subunits. In this thesis, we proposed a framework for study on the metabolic networks evolution. As an extension of previous works, a novel model of metabolic network is reconstructed. It is used to investigate the metabolic networks evolution with an integrative statistical analysis. Based on our statistical analysis results, we derived some major disciplines to reveal the mechanisms of the metabolic network evolution, and gave some insights for metabolic pathways specific studies. This research is parts of a further study of Dr. Yeang's previous works, and also describes an integrative procedure of a statistical analysis on metabolic network evolution studies which could be extend to give more applications.