近年來，細菌頑強的抗藥性對於人類健康及疾病治療構成了嚴重威脅，雖然
全球的生物研究實驗室針對此議題發展出各式對抗細菌抗藥性的方法，但由於無
法全盤掌握細菌對抗藥物之機制及生物路徑，故此問題仍無法完全解決且持續在
研究。
為了解決此問題，我們利用物種的蛋白質互動網路，經由漸進式搜尋演算法
來擷取有關細菌對藥物的反應路徑，並利用隨機漫步模型來辨識蛋白質網路中能
有效輔助抗生素殺死細菌的共同標的─co-target。
我們選擇具有高度感染力且接受Isoniazid (INH) 及Ethionamide (ETA)兩種抗
生素治療的結核桿菌( Mycobacterium tuberculosis )作為研究來源，發現觸發細菌
的葉酸代謝、脂肪酸代謝及菸鹼胺腺嘌呤二核苷酸相關過程對於細菌在施予抗生
素時的存活有很大的響力，結核桿菌有關於藥物排出幫浦機制在此兩種抗生素治
療成為主要抗藥機轉，結果顯示乙醯輔酶A 羧化酶與葉酸代謝、脂肪酸代謝有關
聯，且在細菌抗藥機制中有很強的影響性。
實驗分析的結果與已發表的文獻一致，我們發現關於調控富含甘胺酸細胞膜
之基因、三磷酸腺苷能量代謝之基因，以及與組成細胞壁之生物代謝過程有關連
之基因可能為有效的共同標的，能用以輔助對抗結核桿菌之抗藥機制。
關鍵字：抗藥性、A* 漸進搜尋法、共同標的、隨機漫步演算法、結核桿菌

Drug resistance has now posed more severe and emergent threats to human
health and infectious disease treatment. However, the wet-lab approaches alone to
counter drug resistance have so far still achieved limited success in understanding the
underlying mechanisms and pathways of drug resistance.
Our approach applied a heuristic search algorithm in order to extract drug
response pathways from protein-protein interaction networks and used a random walk
model to identify the potential co-target for effective antibacterial drugs. In this paper,
we chose one of the killer infectious diseases, etiological organisms Mycobacterium
tuberculosis (Mtb) as our test bed that was treated with Isoniazid (INH) and
Ethionamide (ETA). We discovered that both of the genes in INH and ETA networks
would facilitate survival related to triggering the processes in mycobactin synthesis,
fatty acid synthesis/metabolism, and NADH-related processes. Efflux pumps appear
to be the major mechanisms of resistance under INH and ETA drug treatment in Mtb.
The results showed that the acetyl-CoA carboxylase is believed to be involved in fatty
acid and mycolic acid biosynthesis and is strongly associated with the drug resistance
mechanisms. Our analysis is consistent with the recent experimental findings and also
found glycine-rich membrane, Adenosine triphosphate energy and cell wall-related
processes to be potential co-targets for countering drug resistance.
III
keywords ： Drug resistance, A* search, Co-target, Random walk,
Mycobacterium Tuberculosis

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