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原文連結
論文資訊
- 類型：已發表論文
- 日期：2008
摘要
Background: Identifying cyclic pathways in chemical reaction 網絡s is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric 網絡s, such as signal transduction and 基因組-scale metabolic 網絡s, has been hampered by the 計算 complexity of the methods currently used. Results: We describe a new algorithm for the identification of cycles in stoichiometric 網絡s, and we compare its performance to two others by exhaustively identifying the cycles contained in the 基因組-scale metabolic 網絡s of H. pylori, M. barkeri, E. coli, and S. cerevisiae. Our algorithm can substantially decrease both the execution time and maximum memory usage in comparison to the two previous algorithms. Co
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