現今蛋白質互動反應資料與生物微晶片的實驗數據非常豐富，但是生物學家對於自然界生物的生物反應路徑還沒有很完整的了解。我們把這樣的生物問題推延成資訊領域中的搜尋問題，在複雜的生物網路中，尋找最佳的生物反應路徑可以視為NP問題。因此，我們從拓譜學的角度提出著色方法應用在生物網路上來解決這問題，並利用A*啟發式搜尋法來加快搜尋的速度，根據生物晶片所得到的基因表現來找出最有可能有反應的蛋白質所組成的路徑。在實驗中，我們成功的重建酵母菌的費洛蒙相關反應路徑，更預測人類前列腺癌的反應路徑，重建出來的路徑和KEGG資料庫有著很高的相似度。從速度上來評估，我們的方法比起之前的研究也比較快速，在尋找長度8的反應路徑時平均只花費6秒的時間，而長度10平均26秒。

With the large availability of protein-protein interaction data, to identify linear biological meaningful pathways in the sense of optimality on likelihood weights is regarded as a NP-problem. We proposed a color-coding method based on the characteristics of biological network topology to solve this problem and applied an A* heuristic search to speed up the color-coding method in order to extract minimum weight of pathways that most likely have protein-protein interactions corresponding to the microarray data. In the experiments, we tested the methods by applying them to the networks of yeast and human prostate cancer and the results showed that we were able to reconstruct known signaling pathways in comparison to the recent KEGG pathway database. Our algorithms are more efficient than the previous ones and can detect optimal and functional enrichment paths of length 8 within 6 seconds and paths of length 10 within 29 seconds on average.

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